UIEC^2-Net: CNN-based underwater image enhancement using two color space - Wang, Yudong; Guo, Jichang; Gao, Huan; Yue, Huihui (Signal processing. Image communication, 2021)
Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the past few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first one to use HSV color space for underwater image enhancement based on deep learning. UIEC^2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show that the proposed method has good performance in both subjective comparisons and objective metrics. The code is available at https://github.com/BIGWangYuDong/UWEnhancement. •An end-to-end CNN-based underwater image enhancement using RGB and HSV color space is proposed. We are the first to use HSV color space for underwater image enhancement based on deep learning.•A piece-wise linear scaling curve is learned to adjust image properties in HSV color space.•Using differentiable RGB and HSV color space conversions to permit the end-to-end learning.•Our model has better generalization ability and gets better results on real-world underwater image datasets.