@inproceedings{4febca4c27c1428b8246d76153ec87be,
title = "A Fast Algorithm for Image Deconvolution Based on a Rank Constrained Inverse Matrix Approximation Problem",
abstract = "In this paper, we present a fast method for image deconvolution, which is based on the rank constrained inverse matrix approximation (RCIMA) problem. The RCIMA problem is a general case of the low-rank approximation problem proposed by Eckart-Young. This new algorithm, so-called the fast-RCIMA method, is based on tensor product and Tikhonov{\textquoteright}s regularization to approximate the pseudoinverse and bilateral random projections to estimate the rank constrained approximation. The fast-RCIMA method reduces the execution time to estimate optimal solution and preserves the same accuracy of classical methods. We use training data as a substitute for knowledge of a forward model. Numerical simulations on measuring execution time and speedup confirmed the efficiency of the proposed method.",
keywords = "Fast algorithm, Image deconvolution, Pseudoinverse, Rank constrained, Speedup",
author = "Pablo Soto-Quiros and \{Jose Fallas-Monge\}, Juan and Jeffry Chavarr{\'i}a-Molina",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th International Congress on Information and Communication Technology, ICICT 2021 ; Conference date: 25-02-2021 Through 26-02-2021",
year = "2022",
doi = "10.1007/978-981-16-2380-6\_15",
language = "Ingl{\'e}s",
isbn = "9789811623790",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "165--176",
editor = "Xin-She Yang and Simon Sherratt and Nilanjan Dey and Amit Joshi",
booktitle = "Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021",
}