Submarine Sonar Image Filtering And Segmentation Based On Markov Field

Sonar technology is becoming increas ingly important for the use of submarine resources.As a component of sonar technology,sonar imaging is attracting more and more researchers.The combination of Markov random field(MRF)and maximum a posteriori probability(MAP)is widely used in the field of image processing.In this paper,this model is introduced into the field of submarine sonar image(hereinafter referred to as "image")and divided into three areas : a target light area,a target dark area,and a seafloor reverberation area.There are also speckle noise in the bright and dark areas of the target.This has a major impact on subsequent image processing.First,this paper imp lemented it into an ICM iterative algorithm to noise the image using MAP-MRF model of adaptive coupling coefficient.This improved method not only effectively suppresses speckle noise,but also takes into account the edge retention of the image.Since the convergence speed of the ICM iterative algorithm is prone to local solutions,in this paper we set the initial label class of the image using maximum pseudo estimation.The filtering algorithm is compared with median filtering and Wiener filtering respectively to prove the effectiveness of Markov field submarine sonar image filtering.Second,this paper performed it with the ICM iterative algorithm to segment the image using the modified MAP-MRF model.The improvement of the MAP-MRF model is that the gray scale relationship between the central pixel and the neighboring pixels is introduced into the potential function of the MRF model,and this improvement solves the problem that the Markov field image segmentation edge retention effect is not sufficient.In comparison and analys is of segmentation experiments,the improved segmentation algorithm and the uncorrected segmentation algorithm were compared with the Otsu double threshold method,the maximum entropy threshold method and the FCM clustering method respectively to prove the improved MAP-MRF model image.