Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling

The vision computing techniques are widely used in the marine industry. In order to improve the detection and recognition accuracy of artificial intelligence-robotics, we propose a novel underwater image enhancement algorithm, using a multi-scale fusion approach based on the properties of the human visual system. Our method fuses the results of underwater image enhancement algorithms that deal with the color-casting, sharpness and contrast degradation. The method is further weighted by a human visual system-based image structure map that combines Michaelson-like contrast map, saliency map, dark channel map and exposed map. The multi-scale fusion strategy is used to avoid the artifacts of sharpness blending based on Laplacian image representation. The results show that our algorithm can recover more detail information of dark regions and improve the overall visual quality of underwater images.