Deep Convolutional Dictionary Learning for Multi-modal Image
Denoising
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Shengmin
Yang, Huichao Sun£¬Mingzhu Zhang£¬ Zhonggui Sun*
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School of Mathematical Sciences, Liaocheng University
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Abstract
As a milestone in semantic segmentation, non-local
block (NLB) efficiently enhances the ability of regular convolutional neural
networks in capturing long-range dependencies. From the view of mathematical
modeling, NLB is based on a single Gaussian kernel. Existing works suggest that
multi-kernel methods generally get more powerful performance in edge detection,
which is crucial to image segmentation. Motivated by this consideration, we
design a Multi-kernel Non-local Block (MKNLB). As expected, the proposed MKNLB
exhibits excellent behaviors when being used in semantic segmentation.
Additionally, with the distributive law of matrix multiplication, the complexity
of its implementation is comparable to that of the standard NLB. Theoretical
analyses and preliminary experiments on benchmark datasets both support the same
conclusions.
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Paper
S. Yang, H. Sun, M. Zhang, Z. Sun*. Multi-kernel non-local neural network for semantic segmentation. Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), pp. 1670-1674, SPIE, 2022.
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Results
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Theories
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