Learning Deep CNN Denoiser Prior for Image Restoration

by   Kai Zhang, et al.
Harbin Institute of Technology
The Hong Kong Polytechnic University

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.


page 4

page 7

page 8


A Multiscale Image Denoising Algorithm Based On Dilated Residual Convolution Network

Image denoising is a classical problem in low level computer vision. Mod...

Plug-and-Play Image Restoration with Deep Denoiser Prior

Recent works on plug-and-play image restoration have shown that a denois...

Regularization by architecture: A deep prior approach for inverse problems

The present paper studies the so called deep image prior (DIP) technique...

Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination

Most of the current face hallucination methods, whether they are shallow...

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms

The linear inverse problem is fundamental to the development of various ...

Unrolled Optimization with Deep Priors

A broad class of problems at the core of computational imaging, sensing,...

Differentiable Linearized ADMM

Recently, a number of learning-based optimization methods that combine d...

Please sign up or login with your details

Forgot password? Click here to reset