Boosting with fine-tuning for deep image denoising

Abstract

While deep learning is ruling the image denoising domain in recent years, earlier works primarily focused on design of network architecture or training strategy. In this paper, we raise two questions: how to combine the advantages of traditional iterative methods and deep learning-based approaches, and how to avoid performance degradation caused by inaccurate modeling and estimation of noise. To answer the questions, we integrate recursive strategies and fine-tuning schemes to boost existing deep denoisers in a plug-and-play fashion. Specifically, based on the framework of plug-and-play priors, the image denoising problem is solved with the half quadratic splitting (HQS) algorithm to achieve iterative denoising. Different from the standard solving process, we develop a joint optimization scheme with regard to image restoration and network fine-tuning, realizing the matching between network and noise, thereby enabling better adaptation to the images contaminated by complex non-Gaussian noise. As such, two types of adaptive denoising boosters with convergence guarantee based on the fixed-point strategy and steepest-descent method are obtained. It is demonstrated in the experiments that the proposed schemes provide promising performance on additive white Gaussian noise (AWGN) and real-noise denoising for both supervised and self-supervised deep learning-based image denoising algorithms.