A novel highland and freshwater-circumstance dataset: advancing underwater image enhancement

As an important underlying visual processing task, underwater image enhancement techniques have received a lot of attention from researchers due to their importance in marine engineering and lake ecosystem optimization. However, various underwater image enhancement algorithms have been proposed to be evaluated mainly with marine water body datasets, and it is not clear whether these algorithms can be performed on datasets collected from inland lakes in the freshwaters. To bridge this gap, for the first time, we construct an underwater image dataset for highland and freshwater-circumstances (HFUI) using 1000 real images to complement the underwater image datasets. In addition, we propose an unsupervised underwater image enhancement algorithm (HUFI-Net) specifically for this dataset to correct the sharpness and color of the images. This algorithm, as well as current advanced underwater image enhancement algorithms, was investigated qualitatively and quantitatively using this dataset to evaluate the effectiveness and limitations of various algorithms and to provide novel ideas for future underwater image enhancement research. We also further validate the generalization and effectiveness of the algorithm on the underwater image datasets of the UIEB and the RUIE.