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An Underwater Saliency Detection Method Based on Grayscale Image Information Fusion

The complex underwater environment and poor underwater image quality lead to existing saliency detection results with unclear edges and uneven display of internal structures. This seriously affects the quality of underwater image saliency detection. In order to solve the above problems, this paper proposes an underwater saliency detection method based on grayscale image information fusion. Firstly, the interactive dual-stream encoder is constructed to extract the feature information on the grayscale images and RGB images respectively. Secondly, a multi-level fusion strategy is used to obtain salient features to satisfy the encoding and decoding stages to be able to fully utilize the image features. Finally, the mixed distance loss function is proposed to keep the prediction results pixel-wise and overall-wise accuracy. The experimental results show that: The results on 6 public datasets show that the proposed method generates sharper edges and more complete interiors of salient objects.