Non-local means theory based Perona–Malik model for image denosing

• Published in: Neurocomputing (Amsterdam) 2013-11, Vol.120, p.262-267
• Main Authors: Yang, Min, Liang, Jingkun, Zhang, Jianhai, Gao, Haidong, Meng, Fanyong, Xingdong, Li, Song, Sung-Jin
• Format: Article
• Language: English
• Subjects: Algorithms; Image denoising; Non-local means; Partial Differential Equation; Texture detection
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2012.08.063

Among various kinds of image denoising methods, the Perona–Malik model is a representative Partial Differential Equation based (PDE-based) algorithm which effectively removes the noise as well as having edge enhancement simultaneously through anisotropic diffusion controlled by the diffusion coefficient. However, the unstable behavior of the Perona–Malik model introduces staircasing artifacts in the processed images. To realize less diffusion in the texture region and to get more smooth in flat region while implementing image denoising, we propose an improved Perona–Malik model based on non-local means theory, which assumes that the image contains an extensive amount of self-similarity and uses the similarity between the region around the center pixel and the region outside the center pixel to give a more reasonable description of the image. The improved algorithm is applied on numerical simulation and practical images, and the quantitative analyzing results prove that the modified anisotropic diffusion model can preserve textures effectively while ruling out the noise, meanwhile, the staircasing effects are decreased obviously.