A Machine Learning Approach for Non-blind Image Deconvolution

Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Scholkopf; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1067-1074

Abstract


Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant nonblind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step approach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-ofthe-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur.

Related Material


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[bibtex]
@InProceedings{Schuler_2013_CVPR,
author = {Schuler, Christian J. and Christopher Burger, Harold and Harmeling, Stefan and Scholkopf, Bernhard},
title = {A Machine Learning Approach for Non-blind Image Deconvolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}