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Zongxu Chen

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Proceedings Article | 30 April 2024 Paper

Performance comparison of optimization algorithms for multi-frame blind deconvolution

Wentao Li, Jin Liu, Huizhe Yang, Zongxu Chen, Xuewen Liu, Yonghui Liang

Proceedings Volume 13157, 1315717 (2024) https://doi.org/10.1117/12.3018761

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In order to improve the performance of multi-frame blind deconvolution algorithm, the analysis was conducted on the image restoration quality and convergence rate of the multi-frame blind deconvolution algorithm using Conjugate Gradient + Brent, Conjugate Gradient + Dbrent, Conjugate Gradient + Macopt, and L-BFGS + Wolfe combination optimization algorithms. The mathematical principles of above optimization algorithms were elaborated in detail, and they were introduced into the multi-frame blind deconvolution algorithm to achieve high quality restored images. Theoretical and experimental results indicate that the L-BFGS + Wolfe combination algorithm has the fastest convergence rate, but the restoration quality is lower compared to the other combination algorithms; Compared with the other combination algorithms, the Conjugate Gradient + Brent/Dbrent combination algorithm can obtain higher quality restored images, but its convergence rate is slower; The convergence rate and restoring quality of the Conjugate Gradient +Macopt combination algorithm are between L-BFGS + Wolfe and Conjugate Gradient + Brent/Dbrent.

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