AUIED3K: A New Andaman Underwater Image Enhancement Dataset for Deep Learning-Driven Image Enhancement with Minimum Loss Dehazing

Abstract

Underwater images suffer due to light scattering and color absorption, which is challenging for marine researchers, underwater heritage miners, and aquatic robotics. Due to the unavailability of ample real-world underwater ground truth images, it becomes more challenging to frame a deep learning-based underwater image enhancement model to give clear underwater pictures in the existing models. Existing datasets use synthetic images to generate ground truth. In this paper, we propose a new dataset with 3000 real-world reference images taken at depths up to 5 meters so that the collected images are clear and serve the purpose. For raw images, we propose a new algorithm that considers the parameters for color channels for varying noise and color absorption levels, from clear to turbid. We further propose a CNN-based model named UMLDnet with minimum loss dehazing using scene radiance from background light and transmission map. Compared to the state-of-the-art methods, UMLDnet can generate enhanced images with better PSNR and a much lighter deep-learning network.