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|a 10.1109/TIP.2022.3140918
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|a eng
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1 |
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|a Guo, Lanqing
|e verfasserin
|4 aut
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|a Exploiting Non-Local Priors via Self-Convolution for Highly-Efficient Image Restoration
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 26.01.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Constructing effective priors is critical to solving ill-posed inverse problems in image processing and computational imaging. Recent works focused on exploiting non-local similarity by grouping similar patches for image modeling, and demonstrated state-of-the-art results in many image restoration applications. However, compared to classic methods based on filtering or sparsity, non-local algorithms are more time-consuming, mainly due to the highly inefficient block matching step, i.e., distance between every pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local properties in a unified framework. We prove that the proposed Self-Convolution based formulation can generalize the commonly-used non-local modeling methods, as well as produce results equivalent to standard methods, but with much cheaper computation. Furthermore, by applying Self-Convolution, we propose an effective multi-modality image restoration scheme, which is much more efficient than conventional block matching for non-local modeling. Experimental results demonstrate that (1) Self-Convolution with fast Fourier transform implementation can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed online multi-modality image restoration scheme achieves superior denoising results than competing methods in both efficiency and effectiveness on RGB-NIR images. The code for this work is publicly available at https://github.com/GuoLanqing/Self-Convolution
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|a Journal Article
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| 700 |
1 |
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|a Zha, Zhiyuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ravishankar, Saiprasad
|e verfasserin
|4 aut
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| 700 |
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|a Wen, Bihan
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 22., Seite 1311-1324
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|x 1941-0042
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|g volume:31
|g year:2022
|g day:22
|g pages:1311-1324
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|u http://dx.doi.org/10.1109/TIP.2022.3140918
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