![]() In most filtering applications, the non-linear filter is used in place of the linear filter. Non-linear filters preserve edge information and still suppress noise. Linear filters appropriate output pixels with input neighboring pixels (using a matrix multiplication procedure) to reduce noise. Noise reduction filters are categorized into six (linear, non-linear, adaptive, wavelet-based, partial differential equation (PDE), and total variation filters). The linear, non-linear and non-adaptive filters were the first filters used for image applications. Undoubtedly, image denoising is a hot area of research, encompassing all spheres of academic endeavor. Image denoising methods were used to filter paddy leaf and detect rice plant disease. This noise can reduce the quality of evidence in the image thus, image denoising methods have helped suppress noise in forensic images. Moreover, forensic images do not have a specific kind of noise, they could be corrupted by any kind of noise. Image denoising algorithms have helped to reduce speckle in SAR images. ![]() Synthetic aperture radar (SAR) images provide space and airborne operation in military surveillance. In remotes sensing, denoising algorithms are used to remove salt and pepper, and additive white Gaussian noise. In medical and biomedical imaging, denoising algorithms are fundamental pre-processing steps used to remove medical noise such as speckle, Rician, Quantum, and others. Image denoising methods are used in the field of medical imaging, remote sensing, military surveillance, biometrics and forensics, industrial and agricultural automation, and in the recognition of individuals. AWGN occurs in analog circuitry, while impulse, speckle, Poisson, and quantization noise occur due to faulty manufacturing, bit error, and inadequate photon count. Interestingly, the most discussed noise in literature is the: additive white Gaussian noise (AWGN), impulse noise, quantization noise, Poisson noise, and speckle noise. A major problem in image denoising is how to distinguish between noise, edge, and texture (since they all have high-frequency components). Image denoising procedures remove noise and restore a clean image. Nowadays, the process of restoring information from noisy images to obtain a clean image is a problem of urgent importance. Hence, image denoising is a fundamental aspect which strengthens the understanding of image processing task.ĭue to the increasing generation of digital images captured in poor conditions, image denoising methods have become an imperative tool for computer-aided analysis. Noise adversely affects image processing tasks (such as video processing, image analysis, and segmentation) resulting in wrong diagnosis. In image processing, image noise is the variation in signal (in random form) that affects the brightness or color of image observation and information extraction. Environmental, transmission, and other channels are mediums through which images are corrupted by noise. Images are corrupted with noise in the process of acquisition, compression, and transmission. In the last decade, the utilization of images has grown tremendously. Potential challenges and directions for future research were equally fully explicated. Previous and recent papers on image denoising with CNN were selected. We proposed a review of image denoising with CNN. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. Motivations and principles of CNN methods were outlined. Several CNN image denoising papers were selected for review and analysis. Popular datasets used for evaluating CNN image denoising methods were investigated. ![]() Different CNN methods for image denoising were categorized and analyzed. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. These methods used different datasets for evaluation. Several CNN methods for denoising images have been studied. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Image denoising faces significant challenges, arising from the sources of noise. ![]()
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