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A study on moving image noise removal using 3D and time-domain total variation regularization method
Author(s): Tsubasa Munezawa; Tomio Goto; Satoshi Hirano; Son Lam Phung
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Paper Abstract

In recent years, in order to display hi-vision broadcast the next generation displays, super resolution techniques for improving image resolution are demanded. In addition, with the spread of digital cameras and smartphones, people have more opportunities to handle camera images. In particular, images of surveillance cameras are required to obtain highdefinition output by removing noise. In this paper, in order to avoid the adverse effect of image quality deterioration when emphasizing noise mixed image which is a problem of super resolution processing, we examine a noise removal method before super resolution processing. In our proposed method, Total Variation regularization, which is decomposed into structure and texture components, is extended in direction of time axis. As a result, moving images can be decomposed into structure moving images and texture moving images. In theory, it is thought that noise components with large value of Total Variation is shift to texture components. Furthermore, we aim for separation of texture components and noise, and aim for acquisition of high-definition moving images. We verify the performance of our proposed method by comparing it with the BM3D method, which is regarded as the highest performance for moving image noise removal processing.

Paper Details

Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 1104913 (22 March 2019); doi: 10.1117/12.2521353
Show Author Affiliations
Tsubasa Munezawa, Nagoya Institute of Technology (Japan)
Tomio Goto, Nagoya Institute of Technology (Japan)
Satoshi Hirano, Nagoya Institute of Technology (Japan)
Son Lam Phung, Univ. of Wollongong (Australia)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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