Sonar image denoising based on HMT model in morphological wavelet domain
Enfang Sang, Zhengyan Shen, Hongyu Bian, Yuanshou Li · 2010
Sonar images are susceptible to noise pollution that results in low contrast. And sonar image denoising technology is the key for subsequent target recognition. In this paper, an image denoising algorithm using wavelet transform was studied. Firstly, we constructed a morphological mean wavelet for gray image processing. Then the noisy sonar image was trained by the Hidden Markov Tree model in the morphological wavelet domain. According to the characteristics of the morphological mean wavelet, we classified multiresolution analysis of the noisy image in different directions, and removed noise according to the training result with Bayesian estimation. Finally, a desired denoising effect could be obtained by computing the average of different reconstructed images. Computer experiments show that our denoising algorithm can remove Gaussian noise of sonar image effectively. Compared with some classical wavelet denoising methods, image details are retained better.