Adaptive Underwater Image Enhancement Guided by Generalized Imaging Components

Underwater images often exhibit strong color distortion and hazing due to various degradation factors. A majority of algorithms provide color correction for underwater images, but the color is not as vivid as it could be. In order to solve this problem, we propose a method known as adaptive underwater image enhancement guided by generalized imaging components (AUIE-GIC). To the best of our knowledge, this is the first method of utilizing deep learning to develop a generalized imaging model. The proposed method contains two stages: component generation and component guided learning. In the first stage, we obtain the components of a learning-based generalized imaging model. In the second stage, attenuation and transmission features are used to adjust color and semantic information. We propose the attenuation attention module (AAM), and transmission attention module (TAM), which can highlight heavily degraded areas. Finally, the refined network is able to restore underwater images that are rich in semantics and vivid in color. Based on further evaluation and analysis, AUIE-GIC is demonstrated to provide superior performance when compared with state-of-the-art (SOTA) methods.