Denoising Prior Driven Deep Neural Network for Image Restoration

TL;DR

This paper introduces a novel deep neural network for image restoration that combines denoising algorithms with observation model priors, achieving state-of-the-art results across multiple IR tasks.

Contribution

It proposes a denoising prior driven deep neural network that integrates observation model priors with denoising modules for improved image restoration.

Findings

Achieves state-of-the-art results in image denoising, deblurring, and super-resolution.

Efficient iterative process unfolded into a trainable deep network.

Joint optimization of denoisers and back-projection modules enhances performance.

Abstract

Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for achieving further improvements. While most existing DNN-based methods solve the IR problems by directly mapping low quality images to desirable high-quality images, the observation models characterizing the image degradation processes have been largely ignored. In this paper, we first propose a denoising-based IR algorithm, whose iterative steps can be computed efficiently. Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies. A convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images is proposed. As such, the proposed network not only exploits the powerful denoising ability of DNNs, but also leverages the prior of the observation model. Through end-to-end training, both the denoisers and the BP modules can be jointly optimized. Experimental results on several IR tasks, e.g., image denoisig, super-resolution and deblurring show that the proposed method can lead to very competitive and often state-of-the-art results on several IR tasks, including image denoising, deblurring and super-resolution.