Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --

Abstract

Image denoising -removal of additive white Gaussian noise from an image -is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, ten years ago, these achievements have led some researchers to suspect that "Denoising is Dead", in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into the realm of image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today's ability for noise suppression in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern Artificial Intelligence (AI) alternatives in design of image denoisers.The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the second part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using image denoisers for service of other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the (strange?) idea that denoising and other inverse problems might not have a unique solution, as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high perceptual quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This is a survey paper, and its prime goal is to provide a broad view of the history of the field of image denoising and closely related topics in image processing. Our aim is to give a better context to recent discoveries, and to the influence of the AI revolution in our domain.

Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning

“…In image processing, neural networks are one of the most promising approaches to denoising images. There are various types of DL [7,44,45] architectures available for image denoising. A fully symmetric convolutional-deconvolutional network (FSCN) is presented for image denoising in [15].…”

“…As noise, edge, and texture are high-frequency components; it is difficult to distinguish them in the denoising process, and some details may be lost as a result. In general, recovering meaningful information from noisy images to obtain high-quality images has become an increasingly important problem [7].…”

Statistical physics of learning in high-dimensional chaotic systems

“…This is at odds with artificial neural networks (ANNs) at the basis of the deep learning revolution. ANNs are high-dimensional networks that are typically trained to solve an optimization problem, be it to classify images [4], denoise them [5] or generate synthetic images [6,7]. Generally, the feed-forward structure of the architecture is very helpful since it allows the implementation of gradient-based optimization algorithms, such as stochastic gradient descent through backpropagation.…”