Abstract
Among various kinds of image denoising methods, the Perona–Malik model is a representative Partial Differential Equation (PDE) based algorithm which effectively removes the noise as well as having edge enhancement simultaneously through anisotropic diffusion controlled by the diffusion coefficient. However, Partial Differential Equations (PDE) is good at removeling Gaussian noises, but it is not an ideal method to deal with salt-and-pepper noise. To realize less diffusion in the texture region and more smooth in flat region while implementing image denoising, this paper propose an improved Perona–Malik model based on new diffusion function which change with the number of iterations. 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 PSNR are increased obviously.
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Acknowledgments
This work is partially supported by National Natural Foundation Project (61304199), The Ministry of science and technology projects for Hong Kong and Maco (2012DFM30040), Major projects in Fujian Province (2013HZ0002-1,2014YZ0001).
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Xiong, B., Gan, Z., Zou, F., Gao, Y., Du, M. (2017). Method for Noises Removel Based on PDE. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_19
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DOI: https://doi.org/10.1007/978-3-319-48499-0_19
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