Image denoising via a non-local patch graph total variation


Autoři: Yan Zhang aff001;  Jiasong Wu aff001;  Youyong Kong aff001;  Gouenou Coatrieux aff004;  Huazhong Shu aff001
Působiště autorů: LIST, the Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China aff001;  Centre de Recherche en Information Biomédicale Sino-Français, Nanjing, China aff002;  International Joint Research Laboratory of Information Display and Visualization, Southeast University, Ministry of Education, Nanjing, China aff003;  IMT Atlantique, Inserm, LaTIM UMR, Brest, France aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226067

Souhrn

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).

Klíčová slova:

Algorithms – Baboons – Boats – Gaussian noise – Image processing – Imaging techniques – Optimization – Scientific publishing


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Článek vyšel v časopise

PLOS One


2019 Číslo 12