A  multi-images variational Bayesian super-resolution reconstruction method based dual sparse priors

Shuang Gao; Yongqiang Zhang; Bin Bai; Zheng Tan; Guohua Liu; Hao Wang; Zengshan Yin

doi:10.1117/12.2617348

15 February 2022 A multi-images variational Bayesian super-resolution reconstruction method based dual sparse priors

Shuang Gao, Yongqiang Zhang, Bin Bai, Zheng Tan, Guohua Liu, Hao Wang, Zengshan Yin

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121664Q (2022) https://doi.org/10.1117/12.2617348
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a novel prior model to overcome the under-constraint of non-edge regions of image due to total variation prior, so the generation and spread of noise are further suppressed. We combine the weighted total variation model and L1 norm model, achieving a variational bayesian super-resolution reconstruction method based dual sparse priors. The super-resolution results of the simulation data and real data demonstrate that our algorithm is more effective and stable than the same type of other methods.

Citation Download Citation

Shuang Gao, Yongqiang Zhang, Bin Bai, Zheng Tan, Guohua Liu, Hao Wang, and Zengshan Yin "A multi-images variational Bayesian super-resolution reconstruction method based dual sparse priors", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121664Q (15 February 2022); https://doi.org/10.1117/12.2617348

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