Robustness in Deep Learning: Single Image Denoising using Untrained Networks

Abstract

Deep Learning has become one of the cornerstones of today’s AI advancement and research. Deep Learning models are used for achieving state-of-the-art results on a wide variety of tasks, including image restoration problems, specifically image denoising. Despite recent advances in applications of deep neural networks and the presence of a substantial amount of existing research work in the domain of image denoising, this task is still an open challenge. In this thesis work, we aim to summarize the study of image denoising research and its trend over the years, the fallacies, and the brilliance. We first visit the fundamental concepts of image restoration problems, their definition, and some common misconceptions. After that, we attempt to trace back where the study of image denoising began, attempt to categorize the work done till now into three main families with the main focus on the neural network family of methods, and discuss some popular ideas. Consequently, we also trace related concepts of over-parameterization, regularisation, low-rank minimization and discuss recent untrained networks approach for single image denoising, which is fundamental towards understanding why the current state-of-art methods are still not able to provide a generalized approach for stabilized image recovery from multiple perturbations.