Abstract
Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and specific kind of noise using a devoted deep-learning model. Lack of generalization is a major limitation of these models. They cannot be extended to filter image noises other than those for which they are designed. This study deals with the design and training of a generalized deep learning denoising model that can remove five different kinds of noise from any digital image: Gaussian noise, salt-and-pepper noise, clipped whites, clipped blacks, and camera shake. The denoising model is constructed on the standard segmentation U-Net architecture and has three variants—U-Net with Group Normalization, Residual U-Net, and Dense U-Net. The combination of adversarial and L1 norm loss function re-produces sharply denoised images and show performance improvement over the standard U-Net, Denoising Convolutional Neural Network (DnCNN), and Wide Interface Network (WIN5RB) denoising models.
Highlights
Digital images inevitably become corrupted by undesirable noise in the process of acquisition, compression, storage, and transmission
In generating noisy images as training set for denoising learning, each image in the training set of ADE20K was processed with a particular noise that was selected from 5 kinds of noise: Gaussian noise, salt-and-pepper noise, clipped whites, clipped blacks and camera shake
U-Net with Gn, Residual U-Net and Dense U-Net contributed to improve the average results of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for both the loss objectives
Summary
Digital images inevitably become corrupted by undesirable noise in the process of acquisition, compression, storage, and transmission. In real-world applications, especially in dealing with digital contents, a kind of deep learning architecture called the Convolutional Neural Network (CNN) is widely adopted. It showed outstanding performance at the 2012 ImageNet Large-Scale Visual Identity Competition (ILSVRC) [14]. Medical diagnoses, and security are some of the most sensitive areas in which denoising is of paramount importance It is with this motivation that many deep-learning studies are devoted to denoising digital images. A comparative study of the three proposed deep denoising architectures models and their respective loss objectives has obtained the following results:.
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