Open AccessPerformance of super-resolution algorithms under applicative noise
Author(s) -
С. В. Саввин,
А. А. Сирота
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1479/1/012080
Subject(s) - computer science , noise (video) , algorithm , resolution (logic) , frame (networking) , set (abstract data type) , sequence (biology) , segmentation , markov chain , artificial intelligence , image (mathematics) , pattern recognition (psychology) , machine learning , telecommunications , biology , genetics , programming language
The paper considers the problem of multi-frame super-resolution under applicative noise which generates distributed regions of outlying observations in low resolution images. The analysis of existing solutions is performed. They include algorithms based on spin-glass models and Markov random fields used to remove applicative noise. The authors suggest their own approach, which involves using a recurrent algorithm of quasi-linear optimal filtering of a sequence of low resolution images together with superpixel segmentation performed in order to determine the regions damaged by applicative noise. The considered algorithms are compared as applied to a set of test images. The results of the experiment demonstrate that the suggested approach allows for more accurate recovery of HR images than the existing analogues.