Deep Learning on Image Denoising: An overview <s> Deep learning techniques for additive white noisy-image denoising <s> Images are often degraded by noises. Noise can occur during image capture, transmission, etc. Noise removal is an important task in Image processing. In general the results of the noise removal have a strong influence on the quality of the image processing technique. Several techniques for noise removal are well established in color image processing. The nature of the noise removal problem depends on the type of the noise corrupting the im age. In the field of image noise reduction several linear and non linear filtering methods have been proposed. Denoising of image is very important and inverse problem of image processing which is useful in the areas of image mining, image segmentation, pattern recognition and an important preprocessing technique to remove the noise from the naturally corrupted image by the different types of noises. The wavelet techniques are very effective to remove the noise also use of its capability to confine the power of a signal in little convert of energy values. This Paper reviews on various noises like Salt and Pepper noise, Gaussian noise etc and various techniques available for denoising the color image. <s> BIB001 </s> Deep Learning on Image Denoising: An overview <s> Deep learning techniques for additive white noisy-image denoising <s> In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection. The starting point of this paper is the observation that unrolled iterative methods have the form of a CNN (filtering followed by pointwise non-linearity) when the normal operator (<inline-formula> <tex-math notation="LaTeX">$H^{*}H$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$H^{*}$ </tex-math></inline-formula> is the adjoint of the forward imaging operator, <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> image on the GPU. <s> BIB002
Due to insufficiency of real noisy images, additive white noisy images (AWNI) have been widely used to train the denoising model BIB002 . The AWNI includes Gaussian, Poisson, Salt, Pepper and multiplicative noisy images BIB001 . Moreover, there are a lot of deep learning techniques (i.e. CNN/NN, the combination of CNN/NN and common feature extraction methods and the combination of optimization method and CNN/NN) for AWNI denoising. It is known that denoising methods should be compared in the same standard. However, additive white noise include Gaussian, Poisson, low-light noise, salt and pepper noise with different noise levels has big difference. Also, different tools of different methods have influence on denoising results. For the reasons above, we choose typical Gaussian noise to test the denoising performance of different methods. Additionally, most of denoising methods use PSNR as quantitative index. Thus, we use the BSD68, Set12, CBSD68, Kodak24 and McMaster to test the denoising performance of deep learning techniques for additive white noisy-image denoising as follows. For quantitative analysis, Table 8 shows that PSNR values of different networks with different noise levels for gray additive white noisy image denoising. To test the ability of dealing with single gray additive white noisy image from different networks, the Set12 is used to conduct experiments as illustrated in Table 9 . Table 10 proves the denoising performance of different methods for color additive white noisy image denoising. Additionally, we use Table 11 to present the efficiency of different methods for image denoising. For qualitative analysis, we magnify one area in the latent clean image from different methods as observation. As shown Figs. 7-10, the observed area is clearer, the corresponding method has better denoising performance.