Iterative Fractional Integral Denoising Based on Detection of Gaussian Noise
Yuanxiang Jiang, Rui Yuan, Yuqiu Sun, Jinwen Tian · Journal of Software · 2018
In an image with noise, any operation of denoising for a non-noise pixel will change original information.Recent studies show that the denoising algorithms based on noise achieve impressive performance.Meanwhile, because of the characteristics of fractional calculus, the edge information will be retained, and the smooth texture is enhanced while noise is removed.In this paper, an iterative fractional integral denoising algorithm based on noise is proposed.To begin with, we introduce and analyze the noise detection algorithm based on fractional differential gradient and fractional integral denoising from the theoretical point of view.In particular, logical product is made through image of fractional differential gradient to obtain noise position image, thus achieving noise detection.Next, fractional integral denoising algorithms based on tradition and noises are finished.Then, iterative algorithm is used to do multiple searches of noise and integral denoising.In addition, several traditional denoising algorithms and denoising based on noise points are compared to confirm the practicability and feasibility of noise detection algorithm as well as the effectiveness of denoising algorithms based on noise.Finally, different denoising methods are compared to show the characteristic of iterative fractional integral denoising based on noise.By comparing the image visualization and evaluation parameters after processing, it is shown from the experiment results that the method proposed in this paper has good effect of denoising in both subjective and objective aspects.