Noise-Blind Image Deblurring

Meiguang Jin, Stefan Roth, Paolo Favaro; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3510-3518

Abstract


We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form. It avoids the degeneracy of Maximum a-Posteriori (MAP) estimates and leads to an effective noise-adaptive scheme. Moreover, we drastically accelerate our algorithm by using Majorization Minimization (MM) without introducing any approximation or boundary artifacts. We further speed up convergence by turning our algorithm into a neural network termed GradNet, which is highly parallelizable and can be efficiently trained. We demonstrate that our noise-blind formulation can be integrated with different priors and significantly improves existing deblurring algorithms in the noise-blind and in the known-noise case. Furthermore, GradNet leads to state-of-the-art performance across different noise levels, while retaining high computational efficiency.

Related Material


[pdf] [Supp]
[bibtex]
@InProceedings{Jin_2017_CVPR,
author = {Jin, Meiguang and Roth, Stefan and Favaro, Paolo},
title = {Noise-Blind Image Deblurring},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}