Expectation–Maximization Based Image Fusion Algorithm for Detection of Underwater Targets from SONAR Images

With the advancement of technology, the imaging sonars have become the reality and their usage has been extensive in the area of obstacle avoidance in respect of Autonomous Underwater Vehicle (AUV). The underwater environment being heterogeneous, the sonar images have a very complex background, low contrast, and deteriorative edges. To locate and identify the underwater objects from the Sonar images, the initial step needs to be undertaken is the segmentation. Several general purpose algorithms have been developed for segmentation of various images. In this paper the various existing image processing techniques in respect of the sonar images are reviewed. As there is no general solution to the image segmentation problem, making use of the available techniques two new algorithms for processing the underwater Sonar images are proposed. In first algorithm segmented images are combined to a single image called image fusion which performs better than the existing methods with PSNR of 38.006. But in this method also edges of the target are missing. Hence another algorithm is proposed in combination with Expectation maximization technique whose PSNR is 41.2634. Therefore, the proposed algorithm is best suitable to the underwater scenario and is also useful for navigation and guidance of underwater vehicles.