Attentive Deep K-SVD Network for Patch Correlated Image Denoising

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Attentive Deep K-SVD Network for Patch Correlated Image Denoising
Title:
Attentive Deep K-SVD Network for Patch Correlated Image Denoising
Journal Title:
2023 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
11 September 2023
Citation:
Liang, Y., Wang, L., Wang, J., & Luo, Y. (2023, October 8). Attentive Deep K-SVD Network for Patch Correlated Image Denoising. 2023 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip49359.2023.10222179
Abstract:
Techniques of dictionary learning and sparse representation are popular in recent study on image denoising, including classic K-SVD and its variants. The extension of K-SVD to its deep structure learned in an end-to-end way shows the state-of-the-art denoising performance with a great computation efficiency. However, we notice that the current learning framework takes images patches as independent samples, which ignores the inherent correlation among the patches. In this paper, we propose a deep K-SVD denoising network with attention mechanism to enhance the correlation within and among the patches. We impose the two-dimensional correlation on the intermediate parameters during the sparse representation procedure to achieve more smoothing and local structure enhanced image features. Extensive numerical experiments using public data are conducted. The results on two datasets show that the proposed network achieves an average improvement of 0.81dB in peak signal-to-noise ratio (PSNR), 1.66% in the structural similarity (SSIM) and more than 90% in the convergence rate comparing to its counterpart, which demonstrate the efficiency and the competitive performance of our proposed network.
License type:
Publisher Copyright
Funding Info:
This work was partially supported by the General Program of National Natural Science Foundation of China (NSFC) under Grant 62276189, and the Fundamental Research Funds for the Central Universities No. 22120220583
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
15224880
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