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The research concerns the validation of the effectiveness of image filtering methods including Wiener Filter and Median Filter. The filters were implemented in Python and the source code is available at:
https://github.com/tranleanh/Wiener-Median-Comparison
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... b) Construct Wiener Filter and apply it to noisy image Results of applying Wiener Filter to restore noisy images are illustrated in figure 6, left-to-right then top-to-bottom, the first three images are blurred and added Gaussian noise with the sigma value of 10, 20, and 30, respectively, and the rest images are the results of applying Wiener Filter correspondingly to the first three degraded images, meanwhile, figure 7 depicts an implementation to calculate rms error of Wiener Filter. ...
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... Linear filters were destined to remove noise in the spatial domain, but they cannot save image textures. Examples of linear filters include mean filtering [30,39] and Wiener filters [75,99] that have been adopted especially for additive white Gaussian noise. However, they can over-smooth images with high noise, easily blur sharp edges and may lead to loss details. ...
Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise.
... This filtering of an observed noisy process, assuming known stationary signal and noise, and additive noise. The Wiener filter decreases the mean to the minimum average square of the distance between the estimated filtered output and the desired signal [13,15]. ...
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Plant diseases are the primary cause of plant destruction and lead to overall yield reduction. Data collection is one of the significant challenges in agriculture due to varying weather and lighting conditions, and it is a complex task in practicality for crops like Coffee. The existing applications which mainly depend on Vegetation Indices (VIs), structural, textural, and color features are more time-consuming and involve domain expertise for accurate data labeling for coffee plant diseases. An adequate data-enhancing model can overcome this issue; hence, understanding deep learning (DL) algorithms, specifically generative adversarial networks (GANs), can facilitate data analysis and thus enhance research in agriculture. In this work, we present an effective data augmentation approach called CoffeeGAN. This model works based on the segmentation of images using the OpenCV color mask algorithm. The algorithm results in the segmentation of diseased spots of the leaves by giving more attention to the Region of Interest (ROI). Then the segmented images with ROI are utilized for the data augmentation process. This work employed an effective Wiener filter for data pre-processing to produce quality images for the data augmentation process. Using pre-trained model with transfer learning approach, compared our model CoffeeGAN with the base CycleGAN model by training augmented data from both models in ResNet-152. The empirical results proved that CoffeeGAN performed better than CycleGAN with an adequate accuracy of 93.4% compared with 92.0% in CycleGAN.KeywordsDeep learning (DL)Data augmentationGenerative adversarial networks (GAN)ResNet-152OpenCV color mask algorithmCoffeeGANCycleGAN
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