JACIII Vol.23 No.2 pp. 313-316
doi: 10.20965/jaciii.2019.p0313

Short Paper:

A Mixed Denoising Algorithm Based on Weighted Joint Sparse Representation

Yu Ping Hu

College of Basic Teaching, Chongqing Water Resoures and Electric Engineering College
No.801, East Section of the Chang Zhou Avenue, Yongchuan, Chongqing 402160, China

June 22, 2018
August 20, 2018
March 20, 2019
A Mixed Denoising Algorithm Based on Weighted Joint Sparse Representation

Mixed denoising algorithm based on WJSR

Joint sparse representation is not ideal for the processing of outliers in image, so a weighted joint sparse representation model for image denoising is proposed. This model introduces a weighted matrix of common information shared by data samples and reduces the influence of outliers. The greedy algorithm based on weighted simultaneous orthogonal matching pursuit is used to approximate the global optimal solution of the model effectively. The weighted noisy image block is used to remove the mixed noise of the image by jointly coding the nonlocal similar image blocks. By combining global priori knowledge and sparse errors into one unified framework, the denoising performance is further improved. Experimental results show that the denoising performance of this method is better than the existing hybrid denoising methods.

Cite this article as:
Y. Hu, “A Mixed Denoising Algorithm Based on Weighted Joint Sparse Representation,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 313-316, 2019.
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Last updated on Apr. 19, 2019