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Inventi Impact - Multimedia

Articles

  • Inventi:emm/55/14
    SALT AND PEPPER NOISE REMOVAL WITH NOISE DETECTION AND A PATCH-BASED SPARSE REPRESENTATION
    Di Guo, Xiaobo Qu, Xiaofeng Du, Keshou Wu, Xuhui Chen

    Images may be corrupted by salt and pepper impulse noise due to noisy sensors or channel transmission errors. A denoising method by detecting noise candidates and enforcing image sparsity with a patch-based sparse representation is proposed. First, noise candidates are detected and an initial guide image is obtained via an adaptive median filtering; second, a patch-based sparse representation is learnt from this guide image; third, a weighted l1-l1 regularization method is proposed to penalize the noise candidates heavier than the rest of pixels. An alternating direction minimization algorithm is derived to solve the regularization model. Experiments are conducted for 30%~90% impulse noise levels, and the simulation results demonstrate that the proposed method outperforms total variation andWavelet in terms of preserving edges and structural similarity to the noise-free images.

    How to Cite this Article
    CC Compliant Citation: Di Guo, Xiaobo Qu, Xiaofeng Du, Keshou Wu, and Xuhui Chen, “Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation,” Advances in Multimedia, vol. 2014, Article ID 682747, 14 pages, 2014. doi:10.1155/2014/682747 Copyright © 2014 Di Guo et al. This article is distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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