The orthogonal matching pursuit algorithm directly samples the image signal by using the sparsity of the image signal. It uses the atom that matches the image signal feature to describe the image, which can better preserve the detailed features of the image. In this paper, an improvement of variable step size and optimized cut-off conditions is made. The experimental results show that the improved algorithm makes the denoised image clearer and have more detailed features.
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This work is supported by Qing Lan Project of Jiangsu Province-China (Grant No. 2017-AD41779), the Fundamental Research Funds for the Central Universities-China (Grant No. 30916011206) and the Six Talent Peaks Project in Jiangsu Province-China (Grant No. 2015-XCL-008).
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Zhang, Z., Chen, X., Liu, L. et al. A sparse representation denoising algorithm for visible and infrared image based on orthogonal matching pursuit. SIViP 14, 737–745 (2020). https://doi.org/10.1007/s11760-019-01606-1
- Image denoising
- Sparse representation
- Matching pursuit