Underwater image enhancement via brightness mask-guided multi-attention embedding

Abstract

Numerous new underwater image enhancement methods have been proposed to correct color and enhance the contrast. Although these methods have achieved satisfactory enhancement results in some respects, few have taken into account the effect of the raw image illumination distribution on the enhancement results, often leading to oversaturation or undersaturation. To solve these problems, an underwater image enhancement network guided by brightness mask with multi-attention embedding, called BMGMANet, is designed. Specifically, considering that different regions in the underwater images have different degradation degrees, which can be implicitly reflected by a brightness mask characterizing the image illumination distribution, a decoder network guided by a reverse brightness mask is designed to enhance the dark regions while suppressing excessive enhancement of the bright regions. In addition, a triple-attention module is designed to further enhance the contrast of the underwater image and recover more details. Extensive comparative experiments demonstrate that the enhancement results of our network outperform those of existing state-of-the-art methods. Furthermore, additional experiments also prove that our BMGMANet can effectively enhance the non-uniform illumination underwater images and improve the performance of saliency object detection in underwater images.