Abstract
A novel and fast nonlocal mean (NLM) image denoising method using a structure tensor for Gaussian noise removal is proposed in this paper to address the difficult problem of the high computing cost of similarity weights. This method uses visual features based on fuzzy theory to measure the similarity between image pixels. First, from grayscale values and structure information in image patches, the similarity index measure is constructed by using a fuzzy theory measure. Then, the structure tensor is used to eliminate a large number of pixels of low similarity by the similarity index of the visual feature, which can improve the operation speed and avoid the filter parameters. Finally, NLM with a structure tensor and the similarity measure of fuzzy metrics is used to denoise the image. Due to the improved method considering the characteristics of the image visual structure, the structural similarity of NLM is improved. Compared with other improved NLM methods, the improved method in this paper has higher performance in denoising and reduces the complexity of the similarity computation and the problem of parameter setting.
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This research was partially supported from Sichuan Education Department Scientific Research Project (15ZB0425).
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Bo, L., Lv, J., Luo, X. et al. A novel and fast nonlocal means denoising algorithm using a structure tensor. J Supercomput 75, 770–782 (2019). https://doi.org/10.1007/s11227-018-2611-3
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DOI: https://doi.org/10.1007/s11227-018-2611-3