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Effective Single Underwater Image Enhancement by Fusion
Abstract
Due to the absorption and scattering, the clarity and the observation of the depth of field of the image which is obtained by underwater photoelectric imaging will be reduced. This paper introduces a new single image enhancement approach based on image fusion strategy. The method first applies the white balance and global contrast enhancement technologies to the original image respectively, then taking these two adapted versions of the original image as inputs that are weighted by specific maps. We obtain the enhanced results by computing the weight sum of the two inputs in a per-pixel fashion. Since we do not employ deconvolution (computationally expensive), the algorithm reduce the execution time and can effectively enhance the underwater image. The experimental results demonstrate that our method can obtain good visual quality.
... For multiple image development modes, a technique requires two polarization images with varying degrees [15], and the other approach uses many images to increase the underwater image clarity. A fusion-based mechanism [16] derived from adding multiple filters over input, and a fusion mechanism [17] applies the techniques such as white balancing and global contrast stretching for UIE. In [18], the authors proposed another fusion-based method to increase contrast and color, including the inclusion of two images from the color compensated and white-adjusted adoption of the original image. ...
... In (18), k1, k2, and k3 are the application dependent parameters; for example, extra weight must be put on k1 for submerged colour correct and k2 to increase the perceptibility. The comparison metrics are shown in Table 1, and the parameters are based on (16), (17), and (18). The larger values correspond to the better result. ...
span id="docs-internal-guid-54b35aa6-7fff-0992-ed4c-aca4d05cfcfa"> Underwater image enhancement (UIE) is an imperative computer vision activity with many applications and different strategies proposed in recent years. Underwater images are firmly low in quality by a mixture of noise, wavelength dependency, and light attenuation. This paper depicts an effective strategy to improve the quality of degraded underwater images. Existing methods for dehazing in the literature considering dark channel prior utilize two separate phases for evaluating the transmission map (i.e., transmission estimation and transmission refinement). Accurate restoration is not possible with these methods and takes more computational time. A proposed three-step method is an imaging approach that does not need particular hardware or underwater conditions. First, we utilize the multi-layer perceptron (MLP) to comprehensively evaluate transmission maps by base channel, followed by contrast enhancement. Furthermore, a gamma-adjusted version of the MLP recovered image is derived. Finally, the multi-scale fusion method was applied to two attained images. The standardized weight is computed for the two images with three different weights in the fusion process. The quantitative results show that significantly our approach gives the better result with the difference of 0.536, 2.185, and 1.272 for PCQI, UCIQE, and UIQM metrics, respectively, on a single underwater image benchmark dataset. The qualitative results also give better results compared with the state-of-the-art techniques. </span
... For the mode of multiple underwater images enhancement, an approach requiring polarization images of different degrees (Schechner and Averbuch, 2007) and another method using multiple images (Narasimhan and Nayar, 2003) are proposed to improve the visibility of underwater images, but the problem inevitably emerges where multiple images cannot be available in practical applications. To address this issue, a fusion-based approach (Ancuti et al., 2012) is developed to blend different filters on a single input, and then a fusion strategy (Fang et al., 2013) is used to apply white balance and global contrast techniques to enhance raw images. Another fusion approach (Ancuti et al., 2018) is introduced for contrast and color promotion, which blends two latent images derived from color-compensated and white-balanced versions of degraded images. ...
... Compared with existing techniques (Schechner and Averbuch, 2007;Narasimhan and Nayar, 2003) using multiple underwater images, the proposed method is fast implemented on a single underwater image without requiring complex information about underwater imaging scenes. And the proposed method can yield decent underwater enhancement results in a single-resolution fashion, distinct from fusion-based approaches (Fang et al., 2013;Ancuti et al., 2018Ancuti et al., , 2011 using several inputs derived from the raw image and their weight maps. (Fu et al., 2014). ...
... For the mode of multiple underwater images enhancement, an approach requiring polarization images of different degrees (Schechner and Averbuch, 2007) and another method using multiple images (Narasimhan and Nayar, 2003) are proposed to improve the visibility of underwater images, but the problem inevitably emerges where multiple images cannot be available in practical applications. To address this issue, a fusion-based approach (Ancuti et al., 2012) is developed to blend different filters on a single input, and then a fusion strategy (Fang et al., 2013) is used to apply white balance and global contrast techniques to enhance raw images. Another fusion approach (Ancuti et al., 2018) is introduced for contrast and color promotion, which blends two latent images derived from color-compensated and white-balanced versions of degraded images. ...
... Compared with existing techniques (Schechner and Averbuch, 2007;Narasimhan and Nayar, 2003) using multiple underwater images, the proposed method is fast implemented on a single underwater image without requiring complex information about underwater imaging scenes. And the proposed method can yield decent underwater enhancement results in a single-resolution fashion, distinct from fusion-based approaches (Fang et al., 2013;Ancuti et al., 2018Ancuti et al., , 2011 using several inputs derived from the raw image and their weight maps. (Fu et al., 2014). ...
This paper develops a Bayesian retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination. First, a simple yet effective color correction approach is adopted to remove color casts and recover naturalness. Then a maximum a posteriori formulation for underwater image enhancement is established on the color-corrected image by imposing multiorder gradient priors on reflectance and illumination. The l1 norm is appropriately used to model piecewise and piecewise linear approximations on the reflectance, and the l2 norm is used to enforce spatial smoothness and spatial linear smoothness on the illumination. Meanwhile, a complex underwater image enhancement issue is turned into two simple denoising subproblems where their convergence analyses are mathematically provided, and their solutions can be derived by an efficient optimization algorithm. Besides, the proposed model is fast implemented on pixelwise operations while not requiring additional prior knowledge about underwater imaging conditions. Final experiments demonstrate the effectiveness of the proposed method in color correction, naturalness preservation, structures and details promotion, artifacts or noise suppression. Compared with several traditional and leading enhancement approaches, the proposed method yields better results on qualitative and quantitative assessments. The superiority of the proposed method can be extended to several challenging applications.
... As light-weight is attenuated once disseminative in water, the clarity of pictures or videos captured underneath water is sometimes degraded to variable degrees. By exploring the distinction in light-weight attenuation between in atmosphere and in water, paper [19] derive a replacement underwater optical model to explain the formation of AN underwater image within the true physical method, then propose an efficient improvement rule with the derived optical model to enhance the perception of underwater pictures or video frames. In our rule, a replacement underwater dark channel springs to estimate the scattering rate, and an efficient methodology is additionally conferred to estimate the background light-weight within the underwater optical model. ...
... Firstly, the depth information of the hazy image is extracted according to the hazy image model and the prior knowledge. Secondly, the transmission ratio of the underwater light is estimated and adjusted, and the underwater light A is also estimated [19]. At last, the Gama adjustment is used to get the final enhancement image. ...
... ) (6) where F is the Fast Fourier Transform (FFT) operator and F() * is the complex conjugate. The FFT diagonalizes derivative operators and this operation avoids very-largematrix inversion in order to acceleration. ...
... But the obvious problem is emerged where multiple images cannot be directly available in practical applications. To address this issue, a fusion-based approach [3] is derived to blend different filters on a single input, and an fusion strategy [16] is used to apply the techniques of white balance and global contrast to enhance original images. Another fusion-based approach [17] is introduced for contrast and color promotion, which blends two latent images derived from color-compensated and white-balanced versions of degraded images. ...
We develop a novel edge-preserving filtering retinex algorithm for single underwater image enhancement, in which gradient domain guided image filtering (GGF) priors of reflection and illumination are embedded into a retinex-based variational framework for promoting image structures and reducing artifacts or noise. We transform an underwater image enhancement issue into a two-phase objective function. We first employ a general retinex-based method to generate guidance reflection and illumination, and then we use GGF to fuse fine structures of guidance reflection and illumination into ideal reflection and illumination. Meanwhile, the l2 norm is efficiently imposed on GGF priors which measure gradient errors between latent and ideal estimations of reflection and illumination. Then we derive an efficient optimization scheme to address the proposed model, which is fast implemented on pixel-wise operations and requires no prior knowledge about imaging conditions. Final experiments demonstrate the effectiveness of the proposed method in structures promotion, artifacts or noise suppression, naturalness and color preservation. Compared with several leading methods, the proposed method yields better subjective results and objective assessments. Furthermore, the utility of our method is extended for enhancing sandstorm and low illumination images.
... For example, Gray World [3] and White Patch [4] are used in color correction. Fang et al. proposed a single image enhancement approach based on image fusion strategy to enhance the underwater image [5]. Li et al. presented a systematic underwater image enhancement method including underwater image dehazing algorithms and a contrast enhancement algorithm for highquality underwater images [6], and Hitam et al. utilized the contrast limit adaptive histogram equalization (CLAHE) to enhance the contrast [7]. ...
To better detect fish in an aquaculture environment, a high-accuracy real-time detection model is proposed. An experimental dataset was collected for fish detection in laboratory aquaculture environments using remotely operated vehicles. To overcome the inaccuracy of the You Only Look Once v3 (YOLOv3) algorithm in underwater farming environment, a suitable set of hyperparameters was obtained through multiple sets of experiments. Then, a real-time image recovery algorithm is applied before YOLOv3 to reduce the effects of both noise and light on images whilst keeping the real-time capability, leading to a mean average precision of 0.85 and frame rate of 17.6 fps, respectively. Finally, compared with the base detection model using only the YOLOv3 algorithm, the enhanced detection model presented results in a reduction of miss detection rate from 23% to only 9% across different environments and with the detection accuracy of the target in different environments being improved from 8% to 37%.
... In particular, this stage usually consists in the blending of the derived input images by means of the computed weight maps, eventually returning the restored image. For example, in Reference [79,80], the authors propose two different methods to enhance the visibility of underwater images, both based on the Achanta's approach. In these papers, the saliency map is exploited, together with luminance and chromatic maps, as a weight map, and it is employed in the processing pipeline to highlight salient regions and make them more prominent in the final output. ...
Underwater survey and inspection are tasks of paramount relevance for a variety of applications. They are usually performed through the employment of optical and acoustic sensors installed aboard underwater vehicles, in order to capture details of the surrounding environment. The informative properties of the data are systematically affected by a number of disturbing factors, such as the signal energy absorbed by the propagation medium or diverse noise categories contaminating the resulting imagery. Restoring the signal properties in order to exploit the carried information is typically a tough challenge. Visual saliency refers to the computational modeling of the preliminary perceptual stages of human vision, where the presence of conspicuous targets within a surveyed scene activates neurons of the visual cortex, specifically sensitive to meaningful visual variations. In relatively recent years, visual saliency has been exploited in the field of automated underwater exploration. This work provides a comprehensive overview of the computational methods implemented and applied in underwater computer vision tasks, based on the extraction of visual saliency-related features.
... Moreover, the restoration or enhancement of different underwater images of the same scene pose a challenge when they are captured in a medium (water) with properties that change due to impurities (clay, sand and salt etc.). Most of the presented approaches in literature either focus on the enhancement, dehazing, restoration of the color degradations of the underwater images [2,8,9,11,14,15,17,30,32]. For an underwater image, a dehazing algorithm attempts the better visibility, whereas an image enhancement algorithm targets a better contrast and a restoration process seeks for degradations. ...
The underwater images suffer from low contrast and color distortion due to variable attenuation of light and nonuniform absorption of red, green and blue components. In this paper, we propose a Retinex-based underwater image enhancement approach. First, we perform underwater image enhancement using the contrast limited adaptive histogram equalization (CLAHE), which limits the noise and enhances the contrast of the dark components of the underwater image at the cost of blurring the visual information. Then, in order to restore the distorted colors, we perform the Retinex-based enhancement of the CLAHE processed image. Next, in order to restore the distorted edges and achieve smoothing of the blurred parts of image, we perform bilateral filtering on the Retinex processed image. In order to utilize the individual strengths of CLAHE, Retinex and bilateral filtering algorithms in a single framework, we determine the suitable parameter values. The qualitative and quantitative performance comparison with some of the existing approaches shows that the proposed approach achieves better enhancement of the underwater images.
... But the obvious problem is emerged where multiple images cannot be directly available in practical applications. To address this issue, a fusion-based approach [1] is derived to blend different filters on a single input, and an fusion strategy [8] is used to apply the techniques of white balance and global contrast to enhance original images. Another fusion-based approach [2] is introduced for contrast and color promotion, which blends two latent images derived from color-compensated and white-balanced versions of degraded images. ...
We develop a novel edge-preserving filtering retinex algorithm for single underwater image enhancement, in which gradient domain guided image filtering (GGF) priors of reflection and illumination are embedded into a retinex-based variational framework for promoting image structures and reducing artifacts or noise. We transform an underwater image enhancement issue into a two-phase objective function. We first employ a general retinex-based method to generate guidance reflection and illumination, and then we use GGF to fuse fine structures of guidance reflection and illumination into ideal reflection and illumination. Meanwhile, the l2 norm is efficiently imposed on GGF priors which measure gradient errors between latent and ideal estimations of reflection and illumination. Then we derive an efficient optimization scheme to address the proposed model, which is fast implemented on pixel-wise operations and requires no prior knowledge about imaging conditions. Final experiments demonstrate the effectiveness of the proposed method in structures promotion, artifacts or noise suppression, naturalness and color preservation. Compared with several leading methods, the proposed method yields better subjective results and objective assessments. Furthermore, the utility of our method is extended for enhancing sandstorm and low illumination images.
... This method was later used for underwater enhancement [82], with some proposed improvements. For example, in [83], the first input image is obtained via a simple linear transformation and the second image is obtained based on guided image filtering from a foggy image. ...
Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. This paper reviews the main techniques of image dehazing that have been developed over the past decade. Firstly, we innovatively divide a number of approaches into three categories: image enhancement based methods, image fusion based methods and image restoration based methods. All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics. Various quality evaluation methods are then described, sorted and discussed in detail. Finally, research progress is summarized and future research directions are suggested.
... Especially it is widely used in ocean exploration, defense, and fish detection [1]. However, the quality of the underwater images is reduced because of the absorption and scattering effects of the underwater environment [2]. Also it may contain distortion and degradation in the form of noise, blur etc. [3]. ...
In this paper, a comparative study between two image fusion algorithm based on PCA and DWT is carried out in underwater image domain. Underwater image fusion is emerged as one of the main image fusion area, here two or more images will be fused by retaining the most desirable characteristics of each underwater images. The DWT technique is used to decompose the input image into four frequency sub bands and the low-low sub band images will be considered in fusion processing. In PCA method significant eigen values will be considered in fusion process to retain the important characteristics of the input images. The results acquired from both experiments are tabulated and compared by considering the statistical measures such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Entropy. Results shows that underwater image fusion based on DWT outperforms the PCA based method.
Attenuation of light in water causes degradation of underwater images. This attenuation is caused by water molecules, suspended particles, and dissolved chemical compounds in water. The attenuation includes scattering and absorption of light in water. Backward scattering and fading of color are two major sources of degradation of underwater images. This paper proposed a method of enhancement of underwater images by providing a solution for degradation because of backward scattering. The proposed method corrects the effect of backward scattering by enhancing contrast of the image by Rayleigh stretching of each color channel using maximum likelihood estimation of scale parameter. After contrast enhancement, loss of energy in the signal is corrected, that recovers information loss caused by contrast enhancement. The results of the proposed method are compared quantitatively with state-of-the-art methods by applying it to underwater dataset. Comparison is done with mean square error (MSE), Structural SIMilarity index (SSIM), and Average Information Entropy (AIE) quality metrics. It is seen that the proposed method in this paper produces best results when compared with state-of-the-art methods.
Nowadays, underwater vehicles are set with visual sensors. But, it is very likely that underwater images can be captured using optic cameras having low quality due to light conditions in real-life appliances. This is helpful to apply image enhancement techniques to enhance visual quality of the imagery as well as improve interpretability and visibility. We establish a novel strategy that efficiently enhances the visibility of underwater images or videos. The research method is buildup on the fusion approach that takes a series of inputs imitative from the initial image or video. Almost, the proposed fusion-based method aims to give a final picture that overcomes the insufficiency existing in degraded input images by employing several weight maps that differentiate the regions characterized by poor visibility. The widespread experiments exhibit usefulness of our result thus the visible range of underwater images or video is considerably increased by improving both sight difference and color appearance. Our proposed methods are luminance, chromatic, salient weight map, and Laplacian pyramid fusion techniques which enhances a good quality for underwater image.
The quality of underwater color image is degraded due to scatting and attenuation of the illumination in the underwater environment. In order to compensate for visual degradation, an underwater color image enhancement method by imitating human visual mechanism of processing color images is proposed. Firstly, the incomplete Beta function is applied to underwater color images to enhance the brightness of the image. At the same time, the improved multi-scale homomorphic filter with a weighted sum of three different scale homomorphic filters, instead of Gaussian filter in the Retinex algorithm, is employed to estimate the illumination image so as to remove image haze. Finally, the histogram for each channel of the RGB color space is analyzed, and the histogram linear quantification algorithm is applied to achieve color balance. The performance of the proposed bionic model is evaluated both subjectively and objectively. Experimental results demonstrate that the algorithm proposed in this paper can improve the definition and balance the color of the underwater image effectively.
In deep sea environment, the quality of underwater imagery is primarily affected with low contrast, blur and color cast due to the absorption and scattering. In order to deal with these discrepancies a framework is proposed in this paper wherein a set of energy functionals is applied on the approximation and the detailed coefficients of the image. The approximation coefficients of RGB components are modified for adjusting the average intensity value of the image followed by the color correction of these coefficients at finer scales. Subsequently, the processing of detailed coefficients are done for improving local contrast of the image. The performance of the proposed method is evaluated qualitatively and quantitatively on three underwater datasets at varying depths. Qualitative analysis is carried out by comparing the hue histogram of input and output images, whereas quantitative analysis comprises of PSNR, Entropy and SSIM quality metrics. The results of the proposed method are compared with state-of-the-art methods. From the obtained outcomes, it is observed that the proposed method significantly removes the color cast by improving the contrast of underwater images in addition to preserving its detailed structural features.
Since the light is absorbed and scattered while traveling in water, color distortion, under-exposure and fuzz are three major problems of underwater imaging. In this paper, a novel retinex-based enhancing approach is proposed to enhance single underwater image. The proposed approach has mainly three steps to solve the problems mentioned above. First, a simple but effective color correction strategy is adopted to address the color distortion. Second, a variational framework for retinex is proposed to decompose the reflectance and the illumination, which represent the detail and brightness respectively, from single underwater image. An effective alternating direction optimization strategy is adopted to solve the proposed model. Third, the reflectance and the illumination are enhanced by different strategies to address the under-exposure and fuzz problem. The final enhanced image is obtained by combining use the enhanced reflectance and illumination. The enhanced result is improved by color correction, lightens dark regions, naturalness preservation, and well enhanced edges and details. Moreover, the proposed approach is a general method that can enhance other kinds of degraded image, such as sandstorm image.
Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima (WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean (µ), standard deviation (σ), mean square error (MSE) and PSNR (peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement. © 2014, Central South University Press and Springer-Verlag Berlin Heidelberg.
Basically, the sharpness and contrast of the video images captured in dusty weather will be significantly degraded and diminished. This paper proposes a novel image enhancement method. First convert the degraded image into fuzzy domain to global PAL fuzzy enhancement; then band-limited histogram equalization is adopted for enhancing the local component in the spatial domain; finally POSHE algorithm is introduced to enhance the details. The experiment results show that this method not only can effectively improve the contrast of dust image, but also enhance the details of edge information and get a good visual effect of the image.
In this paper, a novel method based on translation-invariant wavelet decomposition has been introduced to the pixel level multisensor image fusion. The proposed fusion architecture is related to the “shift-decompose-fuse-shift” technique and consists of many steps. First, the source images to be shifted in both horizontal and vertical directions. The shifted images will be translated into the wavelet domain and by repeating “shift-translate” to get the decomposition of source images. Second, the different subband coefficients of the fused image are combined with the proposed fusion rule. Finally, the fused image will be obtained with the inverse translate and shift. Experimental results demonstrate that the proposed method fuses the useful information from source images and outperforms the discrete wavelet transform (DWT) and stationary wavelet transform (SWT).
In this paper, a novel method based on translation-invariant wavelet decomposition has been introduced to the pixel level multisensor image fusion. The proposed fusion architecture is related to the “shift-decompose-fuse-shift” technique and consists of many steps. First, the source images to be shifted in both horizontal and vertical directions. The shifted images will be translated into the wavelet domain and by repeating “shift-translate” to get the decomposition of source images. Second, the different subband coefficients of the fused image are combined with the proposed fusion rule. Finally, the fused image will be obtained with the inverse translate and shift. Experimental results demonstrate that the proposed method fuses the useful information from source images and outperforms the discrete wavelet transform (DWT) and stationary wavelet transform (SWT).
License Plate Recognition (LPR) combines computer vision technology andpattern recognition technology and plays an important role in Freeway TollSystem, Urban Road Monitoring System and the Intelligent Parking Lot ManagementSystem. Therefore, it has attracted an ever increasing number of scholars fromhome and abroad. Despite many years of unremitting effort which has resulted inbreakthrough achievements, it remains unsatisfactory in meeting real worldapplication requirements. LPR primarily employs pattern recognition and digitalimage process technology. This paper is focused on the study of patternrecognition. The segmented characters are trained utilizing the BP neuralnetwork. Selecting the ideal training set from the usually large sample set wehave is the first step to train a good network which has a high recognitionrate. At present, training sets are randomly selected, which affects theaccuracy of recognition as well as its speed. Thus, selecting the best trainingsets is of uttermost importance. In this paper, Similarity Comparison Samplingmethod is proposed to improve the training results.
Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.
A novel pre-processing filter is proposed for underwater image restoration. Because of specific transmission properties of light in the water, underwater image suffers from limited range, non uniform lighting, low contrast, color diminished, important blur. . .Today preprocessing methods typically only concentrates on non uniform lighting or color correction and often require additional knowledge of the environment. The algorithm proposed in this paper is an automatic algorithm to pre-process underwater images. It reduces underwater perturbations, and improves image quality. It is composed of several successive independent processing steps which correct non uniform illumination, suppress noise, enhance contrast and adjust colors. Performances of filtering will be assessed using an edge detection robustness criterion.
In underwater situations, clarity of images are degraded by light absorption and scattering. This causes one colour to dominate the image. In order to improve the perception of underwater images, we proposed an approach based on slide stretching. The objective of this approach is twofold. Firstly, the contrast stretching of RGB algorithm is applied to equalize the colour contrast in images. Secondly, the saturation and intensity stretching of HSI is used to increase the true colour and solve the problem of lighting. Interactive software has been developed for underwater image enhancement. Results of the software are presented in this paper.
It is known that scattering by particulates within natural waters is the main cause of the blur in underwater images. Underwater images can be better restored or enhanced with knowledge of the point spread function (PSF) of the water. This will extend the performance range as well as the information retrieval from underwater electro-optical systems, which is critical in many civilian and military applications, including target and especially mine detection, search and rescue, and diver visibility. A better understanding of the physical process involved also helps to predict system performance and simulate it accurately on demand. The presented effort first reviews several PSF models, including the introduction of a semi-analytical PSF given optical properties of the medium, including scattering albedo, mean scattering angles and the optical range. The models under comparison include the empirical model of Duntley, a modified PSF model by Dolin et al, as well as the numerical integration of analytical forms from Wells, as a benchmark of theoretical results. For experimental results, in addition to that of Duntley, we validate the above models with measured point spread functions by applying field measured scattering properties with Monte Carlo simulations. Results from these comparisons suggest it is sufficient but necessary to have the three parameters listed above to model PSFs. The simplified approach introduced also provides adequate accuracy and flexibility for imaging applications, as shown by examples of restored underwater images.
The physical characteristic of target detection based on polarization imaging technology is introduced. Application advantages of polarization imaging technology are outlined. The domestic development of target detection research based on polarization imaging technology is concisely summarized and some problems are pointed out. Then the main achievements in this domain for other countries are specified. The research progress is summed up, some issues are put forward and the prospect in this field is predicted.
Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
Blurred underwater image is always an annoying problem in the oceanic engineering. In this paper, we propose an efficient and low complexity underwater image enhancement method based on dark channel prior. Our method employs the median filter instead of the soft matting procedure to estimate the depth map of image. Moreover, a color correction method is adopted to enhance the color contrast for underwater image. The experimental results show that the proposed approach can effectively enhance the underwater image and reduce the execution time. Besides, this method requires less computing resource and is well suitable for implementing on the surveillance and underwater navigation in real time.
Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.
We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.
This papers introduces a new single image dehazing approach. The method employs a fusion-based strategy that takes as inputs two adapted versions of the original image that are weighted by specific maps in order to yield accurate haze-free results. The method computes in a per-pixel fashion being straightforward to be implemented. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.
As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Underwater robotics represents a fast growing research area and promising industry as advanced technologies in various subsystems develop and potential application areas are explored. Great efforts have been made in developing autonomous underwater vehicles (AUVs) to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R & D activities in the AUV community have increased. This paper describes current state-of-the art in the area of underwater robotics focusing on some key subsystems.
Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. In this paper, we introduce a method for salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. We compare our algorithm to five state-of-the-art salient region detection methods with a frequency domain analysis, ground truth, and a salient object segmentation application. Our method outperforms the five algorithms both on the ground-truth evaluation and on the segmentation task by achieving both higher precision and better recall.
SYNOPSIS. Light is characterized by three basic properties: intensity, the frequency of electromagnetic vibration, and polarization. Beneath the surface of the sea each of these properties of light is modified in ways that could affect the behaviors and the underlying physiological and biochemical mechanisms that control the behavior of organisms. How light changes under water in several time domains, such as seasonal, daily, and even shorter periods of time, is described. The correlation between diel shifts in the activity of fishes and marine invertebrates and the daily changes in light under water is described. It is concluded that exactly how light influences these daily shifts in animal activity, and whether or not circadian rhythms also influence the shifts, is, for most species, not known
Imaging in scattering media such as fog and water is important but challenging. Images suffer from poor visibility due to backscattering and signal attenuation. Most prior methods for visibility improvement use active illumination scanners (structured and gated), which are slow and cumbersome. On the other hand, natural illumination is inapplicable to dark environments. The current paper counters these deficiencies. We study the formation of images under wide field (non-scanning) artificial illumination. We discovered some characteristics of backscattered light empirically. Based on these, the paper presents a visibility recovery approach which also yields a rough estimate of the 3D scene structure. The method is simple and requires compact hardware, using active wide field polarized illumination. Two images of the scene are instantly taken, with different states of a camera-mounted polarizer. A recovery algorithm then follows. We demonstrate the approach in underwater field experiments.
It is often the case that only a few sparse sequences of long videos from scientific underwater surveys actually contain important information for the expert. Locating such sequences is time consuming and tedious. A system that automatically detects those critical parts, online or during post-mission tape analysis, would alleviate the expert workload and improve data exploitation. In this paper, a methodology for evaluating the performance of such a system on real data is presented. Interesting sequences are started by changes of visual context. An algorithm to detect significant context changes in benthic videos in real time has been presented by Lebart et al. in 2000. It is used as an illustration for this methodology - its performance is studied and benchmarked on real underwater data, ground truthed by an expert biologist. Various issues relating to the complexity of the problems of automatically analyzing underwater video are also discussed.
Anne-Sophie Capelle-Laizé and Philippe C. Underwater Image Enhancement by Attenuation Inversion with Quaternions[C]//ICASSP
- P Frédéric
- Anne-Sophie Capelle-Laizé
- Frédéric
Multi-Scale Retinex Enhancement Algorithm on Luminance Channel of Color Underwater Image
- Zhang Kai
- Weiqi
- Su
- Kai
The development and analysis of target detection research based on polarization imaging technology
- Li Hailan
- Wang Xia
- Zhang Chuntao
- Hailan
Laplacian Pyramid and Contrast Pyramid Based Image Fusion and Their Performance Comparison
- Zhangming Yu
- Fei Gao
- Yu
Review of Underwater Opto-electrical Imaging Technology and Equipment(II
- Wang Jin W Eiqi
- Xia
- Cao Fengmei