A machine learning approach for nonblind image deconvolution

Figure 1. Removal of defocus blur in a photograph. The true PSF is approximated with a pillbox. Image deconvolution is the ill-posed problem of recover-ing a sharp image, given a blurry one generated by a con-volution. In this work, we deal with space-invariant non-blind deconvolution. Currently, the most successful meth-ods 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 algo-rithm. However, the methods based on this two-step ap-proach do not properly address the fact that the image in-formation 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. 1