Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 31, Issue 3, Pages 829-843Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2020.2990623
Keywords
Out-of-focus blur; image deblurring; generalized Gaussian function; parametric blur kernel; uniform disk kernel; Gaussian kernel
Categories
Funding
- National Natural Science Foundation of China [61371160]
- Zhejiang Provincial Natural Science Foundation of China [LY18F010007]
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This study proposes a more accurate method for describing out-of-focus blur, modeling the blur kernel using a generalized Gaussian (GG) function and simplifying it to a single-parameter model. Experimental results demonstrate the advantages of this method in image deblurring.
Out-of-focus blur is a common image degradation phenomenon that occurs in case of lens defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a uniform disk in previous work. In this paper, we propose that it can be more accurately depicted using the generalized Gaussian (GG) function. This is motivated by the theoretical analysis of the out-of-focus blur and the practical observation of real blur kernels. We show that as the out-of-focus blur kernels are of specific shapes, the GG function can be further simplified to a single-parameter model. We estimate the parameter of the GG blur kernel from image patches containing step edges, and obtain the clear image by non-blind image deblurring. Experimental results validate that the proposed GG blur kernel estimation algorithm outperforms the state-of-the-art ones deploying either parametric (disk and Gaussian) or nonparametric kernels, and consequently benefits the image deblurring process.
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