Image denoising via sparse representation using rotational dictionary

Yibin Tang; Ning Xu; Aimin Jiang; Changping Zhu

doi:10.1117/1.JEI.23.5.053016

8 October 2014 Image denoising via sparse representation using rotational dictionary

Yibin Tang, Ning Xu, Aimin Jiang, Changping Zhu

Author Affiliations +

Funded by: National Natural Science Foundation of China, National Nature Science Foundation of China, Natural Science Foundation of Jiangsu Province China, Natural Science Foundation of Jiangsu Province, China

Journal of Electronic Imaging, Vol. 23, Issue 5, 053016 (October 2014). https://doi.org/10.1117/1.JEI.23.5.053016

Abstract

A dictionary-learning-based image denoising algorithm is proposed in this paper. Since traditional methods seldom take into account the rotational invariance of dictionaries learned from image patches, an improved K-means singular value decomposition algorithm is developed. In our method, the rotational version of atoms is introduced to greedily match the noisy image in a sparse coding procedure. On the other hand, in a dictionary learning procedure, to maximize the diversity of atoms, a rotational operation on the residual error is adopted such that the rotational correlation among atoms is reduced. As the strategy exploits the rotational invariance of atoms, more intrinsic features existing in image patches can be effectively extracted. Experiments illustrate that the proposed method can achieve a better performance than some other well-developed denoising methods.

Citation Download Citation

Yibin Tang, Ning Xu, Aimin Jiang, and Changping Zhu "Image denoising via sparse representation using rotational dictionary," Journal of Electronic Imaging 23(5), 053016 (8 October 2014). https://doi.org/10.1117/1.JEI.23.5.053016

Published: 8 October 2014

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