@misc{10481/84933,
year = {2023},
month = {10},
url = {https://hdl.handle.net/10481/84933},
abstract = {This paper proposes a deep learning-based method for image restoration given an inaccurate knowledge of the degradation. We first show how the impulse response of a Wiener filter can approximate the Moore-Penrose pseudo-inverse of the blur convolution operator. The deconvolution problem is then cast as the learning of a residual in the null space of the blur kernel, which, when added to the Wiener restoration, will satisfy the image formation model. This approach is expected to make the network capable of dealing with different blurs since only residuals associated with the Wiener filter have to be learned. Artifacts caused by inaccuracies in the blur estimation and other image formation model inconsistencies are removed by a Dynamic Filter Network. The extensive experiments carried out on several synthetic and real image datasets assert the proposed method's performance and robustness and demonstrate the advantage of the proposed method over existing ones.},
organization = {Junta de Andalucía   P20_00286, B-TIC-324-UGR20},
organization = {ERDF A way of making Europe},
organization = {Universidad de Granada / CBUA},
publisher = {Elsevier},
keywords = {Robust non-blind image deconvolution},
keywords = {Deep learning},
keywords = {Convolutional neural network},
keywords = {Analytical methods},
keywords = {Moore-Penrose inverse},
title = {Learning Moore-Penrose based residuals for robust non-blind image deconvolution},
doi = {10.1016/j.dsp.2023.104193},
author = {López Tapia, Santiago and Mateos Delgado, Javier and Molina Soriano, Rafael and Katsaggelos, Aggelos K.},
}