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Singular Value Decomposition in Image Compression and Blurred Image Restoration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2018) 10882 LNCS 62-67
DOI: 10.1007/978-3-319-93000-8_8
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Abstract

The singular value decomposition (SVD) is an important and very versatile tool for matrix computations with a variety of uses. The contribution briefly introduces the concept of the SVD and basic facts about it and then describes two classes of its applications in image processing - image compression and blurred image restoration. Calculations are implemented in MATLAB software. Our experiences and the results are presented in the text.

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Fronckova, K., Prazak, P., & Slaby, A. (2018). Singular Value Decomposition in Image Compression and Blurred Image Restoration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10882 LNCS, pp. 62–67). Springer Verlag. https://doi.org/10.1007/978-3-319-93000-8_8

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