Learning Deep CNN Denoiser Prior for Image Restoration

TL;DR

This paper develops fast CNN-based denoisers integrated into model-based optimization to improve image restoration tasks, achieving both efficiency and high-quality results across various inverse problems.

Contribution

It introduces a set of fast CNN denoisers that serve as priors in model-based methods, enhancing performance in multiple low-level vision tasks.

Findings

CNN denoisers achieve competitive Gaussian denoising results

Effective as priors for diverse image restoration applications

Fast inference speeds suitable for practical use

Abstract

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance; in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.