Abstract
Non-local means (NLM) denoising algorithm is a good similarity measure based denoising algorithm for images with repetitive textures. However, NLM cannot handle the large rotation. In this paper, we propose a rotation-invariant and noise-resistant similarity measure based on improved LBP operator, and use it to search for similar image patches. In addition, in order to speed up the algorithm, an automatic selection strategy of similar patches is proposed. Consequently, the self-similarity can be used to obtain more similar patches for denoising. Experiment results demonstrate that the proposed method achieved higher peak signal-to-noise ratio (PSNR) and more visual pleasing results than some state-of-art methods.
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Cai, B., Liu, W., Zheng, Z., Wang, Z. (2015). A New Similarity Measure for Non-local Means Denoising. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_31
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DOI: https://doi.org/10.1007/978-3-662-48558-3_31
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