Blind deconvolution using an improved L0 sparse representation

Pengzhao Ye; Huajun Feng; Qi Li; Zhihai Xu; Yueting Chen

doi:10.1117/12.2072142

2 September 2014 Blind deconvolution using an improved L0 sparse representation

Pengzhao Ye, Huajun Feng, Qi Li, Zhihai Xu, Yueting Chen

Proceedings Volume 9284, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronics Materials and Devices for Sensing and Imaging; 928419 (2014) https://doi.org/10.1117/12.2072142
Event: 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2014), 2014, Harbin, China

Abstract

In this paper, we present a method for single image blind deconvolution. Many common forms of blind deconvolution methods need to previously generate a salient image, while the paper presents a novel L0 sparse expression to directly solve the ill-positioned problem. It has no need to filter the blurred image as a restoration step and can use the gradient information as a fidelity term during optimization. The key to blind deconvolution problem is to estimate an accurate kernel. First, based on L2 sparse expression using gradient operator as a prior, the kernel can be estimated roughly and efficiently in the frequency domain. We adopt the multi-scale scheme which can estimate blur kernel from coarser level to finer level. After the estimation of this level’s kernel, L0 sparse representation is employed as the fidelity term during restoration. After derivation, L0 norm can be approximately converted to a sum term and L1 norm term which can be addressed by the Split-Bregman method. By using the estimated blur kernel and the TV deconvolution model, the final restoration image is obtained. Experimental results show that the proposed method is fast and can accurately reconstruct the kernel, especially when the blur is motion blur, defocus blur or the superposition of the two. The restored image is of higher quality than that of some of the art algorithms.

Citation Download Citation

Pengzhao Ye, Huajun Feng, Qi Li, Zhihai Xu, and Yueting Chen "Blind deconvolution using an improved L0 sparse representation", Proc. SPIE 9284, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronics Materials and Devices for Sensing and Imaging, 928419 (2 September 2014); https://doi.org/10.1117/12.2072142

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