MHF-UIE a multi-task hybrid fusion method for real-world underwater image enhancement

Underwater image quality often deteriorates, posing significant challenges in extracting underwater information and affecting advanced visual tasks, for instance, tasks in various fields such as oceanography, marine biology, underwater exploration, underwater archaeology, environmental monitoring, and marine engineering. To overcome these issues, many recent methods have attempted various techniques. Most of them focus solely on a single factor, such as visibility recovery or contrast enhancement, while neglecting the overall improvement of image quality. In this paper we propose a multi-task hybrid fusion method (MHF-UIE) for real-world underwater image enhancement by tackling problems such as color distortion, poor visibility, and low contrast. This is achieved through color correction using the gray world assumption, visibility recovery via type-II fuzzy sets, and contrast enhancement with curve transformations. Our experimental results indicate that MHF-UIE outperforms 12 state-of-the-art underwater image enhancement algorithms across three datasets, showing exceptional performance in applications like geometric rotation estimation and edge detection. The proposed MHF-UIE method, making it more desirable for various complex underwater scenarios.