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Proceedings Paper

Multispectral demosaicing via non-local low-rank regularization
Author(s): Yugang Wang; Liheng Bian; Jun Zhang
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Paper Abstract

Demosaicing is an essential technique in filter array (FA) based color and multispectral imaging. It aimes to recover missing pixels at different spectrum bands. The existing methods are limited to specific FAs and local regularization. To enhance generalization on different FA structures and improve reconstruction quality, here we present a non-local low-rank regularized demosaicing method, based on the non-local grouped sparsity of natural images. Specifically, the optimization model consists of two parts, including the regularization term of image formation model, and the low-rank term of non-local grouped image patches. The two terms ensure to remove noise and distortion while preserving image details. The model is solved by the weighted nuclear norm minimization and the alternating direction multiplier method framework. Experiments validate that the proposed algorithm has good generalization performance on both different FA patterns and channel numbers. The reconstruction accuracy is improved compared with the existing demosaicing algorithms.

Paper Details

Date Published: 18 November 2019
PDF: 6 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111870V (18 November 2019); doi: 10.1117/12.2538576
Show Author Affiliations
Yugang Wang, Beijing Institute of Technology (China)
Liheng Bian, Beijing Institute of Technology (China)
Jun Zhang, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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