Multiframe blind deconvolution based on bright and dark channel prior

Haotian Ai

doi:10.1117/12.3011644

7 December 2023 Multiframe blind deconvolution based on bright and dark channel prior

Haotian Ai

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294131 (2023) https://doi.org/10.1117/12.3011644
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Blind image restoration is a challenging and valuable subject in the field of computer vision. In this paper, under the framework of Total Variation (TV) regularization term, the bright and dark channel regularization term is introduced into the multi-frame image blind restoration algorithm, aiming at overcoming the shortcomings of low contrast and obvious ringing effect of the restored image on the basis of retaining the advantages of TV regularization. The bright channel regularization term can estimate the blur degree of the degraded image more accurately, while the dark channel regularization term can better resist noise interference. The split Bregman iterative algorithm is used to solve the objective function, and the details of the restored image are compensated in the iterative process, and further. The experimental results show that the algorithm in this paper has a certain deblurring effect, and the peak signal-to-noise ratio of the restored image is improved by about 0.53 dB to approximately1.5 dB, and the structural similarity is improved by about 0.12 to approximately 0.47, with better visual effects.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haotian Ai "Multiframe blind deconvolution based on bright and dark channel prior", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294131 (7 December 2023); https://doi.org/10.1117/12.3011644

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KEYWORDS

Image processing

Image restoration

Point spread functions

Matrices

Blind deconvolution

Deblurring

Convolution

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