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Image denoising via a non-local patch graph total variation

Abstract : Total variation (TV) based models are very popular in image denoising but suffer from some drawbacks. For example, local TV methods often cannot preserve edges and textures well when they face excessive smoothing. Non-local TV methods constitute an alternative, but their computational cost is huge. To overcome these issues, we propose an image denoising method named non-local patch graph total variation (NPGTV). Its main originality stands for the graph total variation method, which combines the total variation with graph signal processing. Schematically, we first construct a K-nearest graph from the original image using a non-local patch-based method. Then the model is solved with the Douglas-Rachford Splitting algorithm. By doing so, the image details can be well preserved while being denoised. Experiments conducted on several standard natural images illustrate the effectiveness of our method when compared to some other state-of-the-art denoising methods like classical total variation, non-local means filter (NLM), non-local graph based transform (NLGBT), adaptive graph-based total variation (AGTV).
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Yan Zhang, Jiasong Wu, Youyong Kong, Gouenou Coatrieux, Huazhong Shu. Image denoising via a non-local patch graph total variation. PLoS ONE, Public Library of Science, 2019, 14 (12), pp.e0226067. ⟨10.1371/journal.pone.0226067⟩. ⟨inserm-02429395⟩

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