Multiframe blind deconvolution based on bright and dark channel prior
Haotian Ai · 2023
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.