Li, X., Wu, S., Xie, S., & Agaian, S. (2023). Dynamic-Clustering Extreme Intensity Prior Based Blind Image Deblurring. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-023-01161-y
Abstract:
In blind image deblurring, feasible solutions have been obtained by exploiting image prior information such as dark channel prior, extreme channel prior, and local minimal intensity prior. The performance highly depends on these priors, which may have poor adaptability to different image contents in real-world applications. For example, these priors only consider the changes in local minimal and maximal intensity pixels in the blurring process and ignore the difference between these changes. In this paper, we propose a novel blind image deblurring approach based on dynamic-clustering extreme intensity prior. Specifically, the patch-wise maximal pixels (PMaxP) prior and patch-wise minimal pixels (PMinP) prior are employed and clustered into two by applying fuzzy c-means (FCM) clustering. The regularizations impose the sparsity inducing on either PMaxP prior or PMinP prior in each patch. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art image deblurring algorithms on synthetic and real-world images.
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Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10851-023-01161-y