Image Denoising Algorithm Based on Local Adaptive Nonlinear Response Diffusion
Yuanxiu Xing, Jiwu Yu, Fan Zhang, Yicheng Gong · IOP Conference Series Materials Science and Engineering · 2020
Abstract We describe an image denoising algorithm to improve the denoising performance and the time efficiency, while keeping the edge texture of the image from being blurred. First, the algorithm discriminates the pixel whether is an edge pixel or a noise pixel according to the similarity region values of the neighborhoods, and calculates the adaptive diffusion coefficients to construct an adaptive influence function, which can control the noise and the edges to have different diffusion speeds. Then, a 5-layer feedback convolution network with 16 small filters in each layer generates the reconstructed image through convolution, diffusion and deconvolution. Last, the algorithm uses multiple noise levels training sets to train and optimize the network parameters, and generates the finial image denoising model. The experimental results show that the proposed algorithm using adaptive diffusion coefficients can maintain the edges and the smaller filter can improve the time efficiency. The algorithm also has higher denoising accuracy for images with unknown noise level by training on multiple-noise-level training sets.