A blind deconvolution method based on L1/L2 regularization prior in the gradient space

Ying Cai; Yu Shi; Xia Hua

doi:10.1117/12.2282346

19 February 2018 A blind deconvolution method based on L1/L2 regularization prior in the gradient space

Ying Cai, Yu Shi, Xia Hua

Proceedings Volume 10607, MIPPR 2017: Multispectral Image Acquisition, Processing, and Analysis; 106070H (2018) https://doi.org/10.1117/12.2282346
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results.

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Ying Cai, Yu Shi, and Xia Hua "A blind deconvolution method based on L1/L2 regularization prior in the gradient space", Proc. SPIE 10607, MIPPR 2017: Multispectral Image Acquisition, Processing, and Analysis, 106070H (19 February 2018); https://doi.org/10.1117/12.2282346

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KEYWORDS

Image processing

Image restoration

Deconvolution

Image quality

Fourier transforms

Image resolution

Denoising

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