Abstract
This paper proposes a new image enhancement algorithm. At first, the paper uses the combination of rough set and particle swarm optimization (PSO) algorithm to distinguish the smooth area, edge and texture area of the image. Then, according to the results of image segmentation, an adaptive fractional differential filter is used to enhance the image. Finally, the experimental results show that the image enhanced by this algorithm has clear edge, rich texture details, and retains the information of the smooth area of the image.
Highlights
Image enhancement is to improve the visual effect of the image
An image segmentation method based on rough set and particle swarm optimization (PSO) is proposed, which accurately segments the image into smooth area, edge and texture area to prepare for the step of image enhancement
An image segmentation algorithm is designed using the characteristics of rough set that can deal with incomplete information
Summary
Image enhancement is to improve the visual effect of the image. It is an important issue in many fields such as pattern recognition, robotics, medical image processing [1,2,3], and remote sensing [4,5,6,7]. [23] combined rough set and kernel PCA method, proposed a 3D MR image denoising algorithm, ref. An image segmentation method based on rough set and PSO is proposed, which accurately segments the image into smooth area, edge and texture area to prepare for the step of image enhancement. According to the local feature information of the image, an adaptive image enhancement algorithm is designed by using fractional differential filter. [14,17,19], the advantage of the algorithm in the paper is that it combines the rough set and PSO methods to accurately segment the image, and carries out targeted enhancement according to the segmentation results.
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