ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC

Salt&pepper noise detection algorithm for color images based on Spearman rank correlation

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.01.009
  • Received Date: 30 November 2018
  • Rev Recd Date: 30 November 2018
  • Publish Date: 31 January 2019
  • To avoid the operation on non-noise pixels, and preserve the original feature information of the image, which can protect the edge details and obtain high quality color images, a noise detection method based on Spearman rank correlation coefficient is designed for color images. Firstly, each pixel of the color image is taken as the point to be detected, and two six-dimensional vectors are constructed using the spatial and color correlation of the color image between the detection point and the adjacent pixel on the left side. Secondly, the Spearman rank correlation coefficient of the two vectors is calculated, and the extreme points of the image are selected by setting the threshold T1, which is used to make comparisons with the Spearman rank correlation coefficients. Then, the comparative results are used to determine all the possible noise and edge points, called the extreme points. The primary point is determined as the extreme point if the Spearman rank correlation coefficient is smaller than the threshold T1. Finally, eight Spearman correlation coefficients between the extreme point and its surrounding eight pixels are calculated, and the threshold value T2 is set, then, comparisons are made between the eight Spearman rank correlation coefficients and T2 to determine whether the extreme point is noise point or edge point. The simulation results show that the reference values of the two parameters optimized by particle swarm optimization algorithm are 0.28
    To avoid the operation on non-noise pixels, and preserve the original feature information of the image, which can protect the edge details and obtain high quality color images, a noise detection method based on Spearman rank correlation coefficient is designed for color images. Firstly, each pixel of the color image is taken as the point to be detected, and two six-dimensional vectors are constructed using the spatial and color correlation of the color image between the detection point and the adjacent pixel on the left side. Secondly, the Spearman rank correlation coefficient of the two vectors is calculated, and the extreme points of the image are selected by setting the threshold T1, which is used to make comparisons with the Spearman rank correlation coefficients. Then, the comparative results are used to determine all the possible noise and edge points, called the extreme points. The primary point is determined as the extreme point if the Spearman rank correlation coefficient is smaller than the threshold T1. Finally, eight Spearman correlation coefficients between the extreme point and its surrounding eight pixels are calculated, and the threshold value T2 is set, then, comparisons are made between the eight Spearman rank correlation coefficients and T2 to determine whether the extreme point is noise point or edge point. The simulation results show that the reference values of the two parameters optimized by particle swarm optimization algorithm are 0.28
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