Dr. Xenwen Liu Profile

Dr. Xenwen Liu

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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.

Proceedings Article | 24 November 2023 Paper

Hybrid MPI-CUDA accelerated multi-frame blind deconvolution algorithm

Zongxu Chen, Xenwen Liu, Yonghui Liang

Proceedings Volume 12935, 129352B (2023) https://doi.org/10.1117/12.3007580

KEYWORDS: Adaptive optics, Point spread functions, Blind deconvolution, Parallel computing, Mathematical optimization

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Multi-Frame Blind Deconvolution (MFBD) algorithm is the mainstream post-processing method for atmospheric turbulence degraded images. The restoration of a single object from multiple images requires several minutes or even tens of minutes, which severely limits MFBD’s practical application. To achieve the goal of fast restoration, this paper proposes hybrid MPI-CUDA (Message Passing Interface-Compute Unified Device Architecture) accelerated MFBD algorithm. In this hybrid programming model, MPI is responsible for assigning computing tasks, and CUDA is responsible for parallel computing. Hybrid MPI-CUDA accelerated MFBD algorithm has been tested on simulated images and achieved satisfactory results. The execution speed for restoring a single high-resolution image from multiple blurred images has been increased by over 50-fold.

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