A novel underwater sonar image enhancement algorithm based on approximation spaces of random sets

Abstract

Underwater environment is complex and random. The images obtained from underwater by sonar always have uneven background gray distribution and fuzzy details of boundary. Hence the low-quality sonar images need to be enhanced before analysis. This paper presents a sonar image enhancement algorithm based on the approximation spaces of random sets. First of all, the knowledge representation of the underwater image is constructed by the approximation spaces of random sets. According to the background knowledge, the image is divided by the upper and lower approximation space of the random set. Then the optimal partition is obtained according to the approximate equivalence relation of the upper and lower approximation. Based on the optimal partition, an improved dark channel theory is presented to enhance each region of the image. After that, sonar images with different backgrounds are used to test the proposed method. The experimental results show that the gray distribution of the sonar image enhanced by this algorithm is more uniform and the boundary details are clearer. The proposed algorithm has the advantage of solving the optimal division for the set of pixels with approximate grayscale. Moreover, the proposed algorithm can get better image enhancement effect in the premise of maintaining the texture of the sonar images.