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This paper presents an optimization based algorithm for underwater image de-hazing problem. Underwater image de-hazing is the most prominent area in research. Underwater images are corrupted due to absorption and scattering. With the effect of that, underwater images have the limitation of low visibility, low color and poor natural appearance. To a...
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... Inspired by Iqbal et al., a two-stage technique for underwater image contrast enhancement and color correction was presented by Ghani et al. [5]. A series of histogram equalization methods (HE [24], AHE [25], and CLAHE [26]) was also used to enhance underwater images [27,28]. Jin et al. [29] combined CLAHE with Gaussian differential pyramids to solve the problem of low-contrast and blurred details in underwater images. ...
Due to light absorption and scattering underwater images suffer from low contrast, color distortion, blurred details, and uneven illumination, which affect underwater vision tasks and research. Therefore, underwater image enhancement is of great significance in vision applications. In contrast to existing methods for specific underwater environments or reliance on paired datasets, this study proposes an underwater multiscene generative adversarial network (UMGAN) to enhance underwater images. The network implements unpaired image-to-image translation between the underwater turbid domain and the underwater clear domain. It has a great enhancement impact on several underwater image types. Feedback mechanisms and a noise reduction network are designed to optimize the generator and address the issue of noise and artifacts in GAN-produced images. Furthermore, a global–local discriminator is employed to improve the overall image while adaptively modifying the local region image effect. It resolves the issue of over- and underenhancement in local regions. The reliance on paired training data is eliminated through a cycle consistency network structure. UMGAN performs satisfactorily on various types of data when compared quantitatively and qualitatively to other state-of-the-art algorithms. It has strong robustness and can be applied to various enhancement tasks in different scenes.
... To resolve the regularization effect, an efficient nonlocal total variation based algorithm is proposed by observing depth factor [52]. Underwater image dehazing has also been proved very effective in the field of image dehazing, one of the algorithm based on fuzzy function based on each color channel is produced [53]. An algorithm based on patch comparison from original image and choosing the best one is presented by [54] Another regularization-based debating algorithm is presented by [55], which is based on transmission map and depth recommencement based regularization. ...
Haze and fog are big reasons for road accidents. The haze occurrence in the air lowers the images quality captured by visible camera sensors. Haze brings inconvenience to numerous computer vision applications as it diminishes the scene visibility. Haze removal techniques recuperate the color and scene contrast. These haze removal techniques are extensively utilized in numerous applications like outdoor surveillance, object detection, consumer electronics, etc. Haze removal is commonly performed under the physical degradation model, which requires a solution of an ill-posed inverse issue. Different dehazing algorithms was recently proposed to relieve this difficulty and has acknowledged a great deal of consideration. Dehazing is basically accomplished through four major steps: hazy images acquisition process, estimation process (atmospheric light, transmission map, scattering phenomenon, and visibility or haze level), enhancement process (improved visibility level, reduce haze or noise level), restoration process (restore enhanced image, image reconstruction). This four-step dehazing process makes it possible to provide a step-by-step approach to the complex solution of the ill-posed inverse problem. Our detailed survey and experimental analysis on different dehazing methods that will help readers understand the effectiveness of the individual step of the dehazing process and will facilitate development of advanced dehazing algorithms. The overall objective of this review paper is to explore the various methods for efficiently removing the haze and short comings of the earlier presented techniques used in the revolutionary era of image processing applications.
... The water light attenuations may include and thus processing of sea imaging data becomes more challenging. Certain studies showed that the existence of certain intrinsic deficiencies is attributed to the appearance of objects and ambient noise in underwater images [49]- [51]. Consequently, it is difficult in a real-time system to distinguish objects from their surroundings in these images. ...
This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model performances based on quantitative metrics and image processing speed. The hyperparameter in the feature-extraction phase was configured based on the learning rate and momentum and further improved based on the adaptive moment estimation (ADAM) optimizer and the function reducing-learning-rate-on-plateau to optimize the model’s training scheme. The optimized YOLOv5s achieved a better performance, with a mean average precision of 98.6% and a high inference speed of 106 frames per second. The ADAM optimizer with a detailed learning rate (0.0001) and momentum (0.99) fine-tuning yielded a sufficient convergence rate (0.69% at 55th epoch) to assist YOLOv5s in attaining a more precise detection for underwater objects.
... Lastly, the paper compares their MSE, PSNR and entropy values with other papers. The authors of [19] used Improved Hybrid Frame Differencing Algorithm (IHFDA) for recognition, while LBP and SVM for classification.The paper proposes the techniques for the endangered gangetic dolphins via SURF, LBF and SVM method. In the initial stages noise removal and conversion to grey-scale is performed. ...
Image enhancement has major applications in the field of aquatic life or marine life scenarios. Especially, when the underwater image is concerned , the changed medium and thus the changes in the behaviour of various properties of the image (object) causes its degradation. Degraded underwater images show some limitations when they are used for any further analysis. So, the enhancement of such images is rightfully apt and at the same time, challenging. One would know that various colorspaces, when applied to an image, represent the image differently, as they are made up of parameter combinations which differ from one another. The same goes with the image enhancement techniques. So firstly, different image enhancement modules used especially for underwater (UW) images including color spaces and image enhancement algorithms are discussed. In today's modern age, image fusion is the emerging field in the area of image processing. Two or more images, with different settings and their respective features, are merged so that some desirable characteristics of every image are retained in the final output image. But the fusion techniques in available literature does not achieve better enhancement. We propose a two stage image enhancement algorithm which first comprises of an underwater 2 Omkar A. Jugade et al. image being applied to two different algorithms separately. Those are color balance + white balance and CLAHE (contrast limited adaptive histogram equalization). Next, the four frequency components obtained from these two operations are fused so as to provide solution to the problem statement. This second stage comprises of an analysis of different wavelet transforms used with different mother wavelets onto a same image in different colorspaces to conclude which of the sets is better-performing. Based on the results from this analysis, the further research on the topic would be guided. The analysis also includes SIFT local feature matching to see the extent of feature retaining in the enhanced, scaled or rotated version of an underwater image. Finally, a comparison of all the techniques/approaches based on the experiments conducted is presented.
... The authors of [8] used Improved hybrid frame differencing algorithm (IHFDA) for recognition,while LBP and SVM for classification [8]. The author C. Akila used Enhanced fuzzy intensification operator for enhancement in [9]. J. Ahn [10] used Retinex Theory and crab recognition for Enhancement and used Bag of Keypoints for classification (2017). ...
Image enhancement has major applications in the field of aquatic life or marine life scenarios. Especially, when the underwater image is concerned, the changed medium and thus the changes in the behaviour of various properties of the image (object) causes its degradation. Degraded underwater images show some limitations when they are used for any further analysis. So, the enhancement of such images is rightfully apt and at the same time, challenging. In this paper, we firstly discuss the different image enhancement modules used especially for underwater (UW) images, which include color spaces and image enhancement algorithms. In today's modern age, an approach called Image Fusion is the emerging field in the area of Image Processing. Two or more images, with different settings and their respective features, are merged so that some desirable characteristics of every image are retained in the final output image. But these fusion techniques in available literature does not achieve better enhancement. The novelty of this paper is about optimizing the fusion with bat optimization algorithm/approach that would proposed so as to provide solution to the problem statement. Finally, a comparison of all the techniques/approaches based on our experiments concludes the better approach for further research.
... Akila and Varatharajan introduced an enhanced fuzzy intensification method for the removal of haze effects to improve the color contrast of underwater images [2]. They proposed this method to prevent the problems of poor natural appearance, low visibility, and color. ...
Exploration of the deep sea and ocean in the marine industry has continued to gain interest in recent years. To get the detailed imaging of deep sea layers, marine vessels and robots are fitted with advanced imaging technologies. There are certain factors like water properties and impurities that affect the quality of the photographs captured by the underwater imaging devices. As sea water absorbs colors, so processing of sea imaging data becomes more challenging. Water light attenuation is a phenomenon that is caused by the absorbance and scattering factors. Certain studies showed that the existence of certain intrinsic shortcomings are attributed to the appearance of objects and ambient noise in underwater images. As a result, it is difficult in a real-time system to distinguish objects from their surroundings in these images. We measures the algorithms performance with respect to various aspects, effect of the hardware and software parts for underwater images and critical review of different underwater image enhancement algorithms. First, we describe some well-known techniques of spatial and frequency domains. Then, we list the existing quantitative measurements which are required to measure the quality of the enhanced image. Finally, the performance of various up-to-date existing methods is compared based on the outcomes of standard quantitative measurements, and factors such as requirements/suitability, and technical aspects, are included. Furthermore, a variety of image databases used for image contrast enhancement is discussed in detail. This study expands the scope for other researchers to understand the important characteristics of different underwater image contrast enhancement methods, and also provides future research directions.
... For real-time object detection techniques, underwater image segmentation and detection is very challenging, so several algorithms such as discriminative regional feature integration by Yafei Zhu et al. [5], background modelling using multi-feature integration framework by Srikanth Vasamsetti et al. [4] and blob analysis by Hailing Zhou et al. [11], color restoration algorithm by D. Lee et al. [7] to counterfeit color degradation, Constant false Alarm Rate and MBES by Aneta Nikolovska et al. [3] to detect OOI in dynamic underwater environment are used in previous researches. For removing haziness in real time underwater images, approaches like enhanced fuzzy intensification operator by C. Akila et al. [8] have already been used, providing efficient object clarity for classification and detection. In case of detection of OOI from moving objects, background subtraction and frame difference methods by Hongkung Liu et al. [10] are used. ...
Underwater Object Detection had been one of the most challenging research fields of Computer Vision and Image Processing. Before Computer Vision techniques were used for underwater imaging, all the tasks associated with object detection had to be done manually by marine scientists making the task one of the most tedious and error prone. For this case, Underwater Autonomous Vehicles (UAV) has been developed to capture real time videos for specific object detection. Using different hardware improvements and using many varied forms of algorithms, classification of objects, mainly living objects had been carried with different AUVs and high-resolution cameras. Conventional object detection methods of Computer Vision fail to provide accurate detection results due to some challenges faced underwater. For such reasons, object detection underwater needs to be robust, real time and fast also being accurate, for which deep learning approaches are introduced. In this paper, all the works here all the trending underwater object detection techniques are discussed in details and a comprehensive comparative study is carried out.
... In addition, the subjective evaluations [87,100] and methods designed for natural image quality evaluations, such as structural similarity index measure (SSIM) [51,70,100,102], patch-based contrast quality index (PCQI) [47,48,51,83], mean square error (MSE) [46,51,84,102,103], PSNR [19,49,51,70,[101][102][103][104], average E [105], contrast to noise ratio (CNR) [19], entropy [70,103,106], discrete entropy and contrast measure (DECM) [103], gradient ratio at visible edges (GAVE) [107], global contrast factor (GCF) [44], and visibility metric based on contrast-to-noise ratio (VM) [44,48], were commonly adopted. Also, the effectiveness of the improvement for some specific processing such as SLAM [19] and feature point matching [105] of underwater images was also considered. ...
Images taken under water usually suffer from the problems of quality degradation, such as low contrast, blurring details, color deviations, non-uniform illumination, etc. As an important problem in image processing and computer vision, the restoration and enhancement of underwater image are necessary for numerous practical applications. Over the last few decades, underwater image restoration and enhancement have been attracting an increasing amount of research effort. However, a comprehensive and in-depth survey of related achievements and improvements is still missing, especially the survey of underwater image dataset which is a key issue in underwater image processing and intelligent application. In this exposition, we first summarize more than 120 studies about the latest progress in underwater image restoration and enhancement, including the techniques, datasets, available codes, and evaluation metrics. We analyze the contributions and limitations of existing methods to facilitate the comprehensive understanding of underwater image restoration and enhancement. Furthermore, we provide detailed objective evaluations and analysis of the representative methods on five types of underwater scenarios, which verifies the applicability of these methods in different underwater conditions. Finally, we discuss the potential challenges and open issues of underwater image restoration and enhancement and suggest possible research directions in the future.
... The take into account the likelihood of extensive blunders in the range estimations because of UDP spread conditions. In relieving the UDP impact, the approach is to consolidate middle of the road area gauges from various subsets of guides [6,7,8]. The novel criteria is proposed for distinguishing the blends that deliver awful gauges. ...
... The overall steps were not changed, and white balance color recovery was added. Some algorithms used different underwater scattering models to evaluate the light intensity [25,26]. Block M et al. [27] proposed an automatic approach based on the dark channel prior model to recover the picture. ...
There are many tasks that require clear and easily recognizable images in the field of underwater robotics and marine science, such as underwater target detection and identification of robot navigation and obstacle avoidance. However, water turbidity makes the underwater image quality too low to recognize. This paper proposes the use of the dark channel prior model for underwater environment recognition, in which underwater reflection models are used to obtain enhanced images. The proposed approach achieves very good performance and multi-scene robustness by combining the dark channel prior model with the underwater diffuse model. The experimental results are given to show the effectiveness of the dark channel prior model in underwater scenarios.
