Low-Weight and Learnable Image Denoising

Image denoising is a well studied problem with an extensive activity that has spread over several decades. Despite the many available denoising algorithms, the quest for simple, powerful and fast denoisers is still an active and vibrant topic of research. Leading classical denoising methods are typically designed to exploit the inner structure in images by modeling local overlapping patches. In contrast, recent newcomers to this arena are supervised neural-network-based methods that bypass this modeling altogether, targeting the inference goal directly and globally, while tending to be very deep and parameter heavy. This work proposes a novel low-weight learnable architecture that embeds in it several of the main concepts from the classical methods, while being trained for best denoising performance. More specifically, our proposed network relies on patch processing, leveraging non-local self-similarity, representation sparsity and a multiscale treatment. The proposed architecture achieves near state-of-the-art denoising results, while using a small fraction of the typical number of parameters. Furthermore, we demonstrate the ability of the proposed network to adapt itself to an incoming image by leveraging similar clean ones.