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 non-blind 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-of-the-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.

Keywords

DeconvolutionBlind deconvolutionComputer scienceArtificial intelligenceImage restorationComputer visionConvolution (computer science)Focus (optics)Image (mathematics)Fourier transformNoise (video)Wiener deconvolutionInvariant (physics)Image processingPattern recognition (psychology)AlgorithmArtificial neural networkMathematics

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Publication Info

Year
2013
Type
article
Pages
1067-1074
Citations
320
Access
Closed

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Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling et al. (2013). A Machine Learning Approach for Non-blind Image Deconvolution. , 1067-1074. https://doi.org/10.1109/cvpr.2013.142

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DOI
10.1109/cvpr.2013.142