Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration

• URL: http://arxiv.org/abs/2006.13714v2
• Date: Mon, 24 May 2021 06:11:12 GMT
• Title: Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration
• Authors: Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar and Bihan Wen
• Abstract summary: We propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. The proposed Self-Convolution can generalize the commonly-used block matching step and produce equivalent results with much cheaper computation. Experimental results demonstrate that Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms.
• Score: 36.22821902478044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Constructing effective image priors is critical to solving ill-posed inverse problems in image processing and imaging. Recent works proposed to exploit image non-local similarity for inverse problems by grouping similar patches and demonstrated state-of-the-art results in many applications. However, compared to classic methods based on filtering or sparsity, most of the non-local algorithms are time-consuming, mainly due to the highly inefficient and redundant block matching step, where the distance between each pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. The proposed Self-Convolution can generalize the commonly-used block matching step and produce equivalent results 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 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 multi-modality image restoration scheme achieves superior denoising results in both efficiency and effectiveness on RGB-NIR images. The code is publicly available at \href{https://github.com/GuoLanqing/Self-Convolution}.

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