Deep Mean-Shift Priors for Image Restoration

被引：0

作者：

Bigdeli, Siavash A. ^{[1
]}

Jin, Meiguang ^{[1
]}

Favaro, Paolo ^{[1
]}

Zwicker, Matthias ^{[1
,2
]}

机构：

[1] Univ Bern, Bern, Switzerland

[2] Univ Maryland, College Pk, MD 20742 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | 2017年 / 30卷

基金：

瑞士国家科学基金会;

关键词：

FRAMEWORK; FIELDS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we introduce a natural image prior that directly represents a Gaussian-smoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution. In addition, we learn the mean-shift vector field using denoising autoencoders, and use it in a gradient descent approach to perform Bayes risk minimization. We demonstrate competitive results for noise-blind deblurring, super-resolution, and demosaicing.

引用

页数：10