Infrared image denoising algorithm based on adaptive dictionary learning

Deqin Shi; Wei Yang; Junshan Li

doi:10.1117/12.902876

8 December 2011 Infrared image denoising algorithm based on adaptive dictionary learning

Deqin Shi, Wei Yang, Junshan Li

Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 80021R (2011) https://doi.org/10.1117/12.902876
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

A novel infrared image denosing algorithm is proposed based on adaptive dictionary learning over sparse and redundant representations. The dictionary which can yield sparse representations is learned from the corrupted infrared image itself, instead of using the prechosen set of basis functions such as curvelet or contourlet. Meanwhile, the over-completed dictionary is updated adaptively in the online learning procedure other than batch learning method to improve the learning performance. And the learning and denoising procedure are fused together into one iterated process naturally and properly. Experimental results demonstrate the effectiveness of the denosing algorithm for infrared images.

Citation Download Citation

Deqin Shi, Wei Yang, and Junshan Li "Infrared image denoising algorithm based on adaptive dictionary learning", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80021R (8 December 2011); https://doi.org/10.1117/12.902876

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