Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74157
Title: Partial deconvolution with inaccurate blur kernel
Authors: Ren, D 
Zuo, W
Zhang, D 
Xu, J 
Zhang, L 
Keywords: Blind deconvolution
Blur kernel estimation
Computational modeling
Deconvolution
E-M algorithm
Estimation error
Image deblurring
Kernel
Robustness
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2018, v. 27, no. 1, p. 511-524 How to cite?
Journal: IEEE transactions on image processing 
Abstract: Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.
URI: http://hdl.handle.net/10397/74157
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2017.2764261
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