Abstract: To address the problem of low contrast and edge blurring in underwater images, a multi-scale wavelet and Tsallis entropy-based underwater image edge detection algorithm is proposed. Firstly, combining the characteristics of multi-scale wavelet decomposition, the open dark channel model is used to remove low-frequency haze and the soft threshold operation is used to reduce high-frequency noise. Secondly, a two-dimensional Gaussian function is used to construct a Gaussian scale space for background estimation to distinguish background from target information. Finally, the optimal threshold is obtained by combining information entropy and Tsallis entropy, and the edge detection image is obtained. Experimental results show that the proposed algorithm can effectively detect the edge contours of degraded underwater images, remove false edge situations, and accurately extract the feature edges of the image. At the same time, tests show that the algorithm performs well in edge detection of atmospheric haze images.

Key words: underwater image, edge detection, multiscale wavelet, Tsallis entropy