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Image denoising using multi-stage sparse representations

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2 Author(s)
Tao Gan ; School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore ; Wenmiao Lu

This paper presents a novel image denoising method based on multiscale sparse representations. The denoising is performed in a multi-stage framework where sparse representations are obtained in different scales to capture multiscale image features. Based on the multi-stage structure, we introduce a new stopping criterion for sparse coding to capture image structures more accurately than previous methods. Furthermore we propose a thresholding technique to effectively avoid artifacts which are usually introduced due to the erroneous pursuit for noise-induced structures. Experimental results demonstrate that the proposed method achieves PSNR performance comparable to other state-of-the-art methods while producing denoised images with superior visual quality.

Published in:

2010 IEEE International Conference on Image Processing

Date of Conference:

26-29 Sept. 2010