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Underwater image enhancement has attracted much attention due to the rise ofmarine resource development in recent years. Benefit from the powerfulrepresentation capabilities of Convolution Neural Networks(CNNs), multipleunderwater image enhancement algorithms based on CNNs have been proposed in thelast few years. However, almost all of these algorithms employ RGB color spacesetting, which is insensitive to image properties such as luminance andsaturation. To address this problem, we proposed Underwater Image EnhancementConvolution Neural Network using 2 Color Space (UICE^2-Net) that efficientlyand effectively integrate both RGB Color Space and HSV Color Space in onesingle CNN. To our best knowledge, this method is the first to use HSV colorspace for underwater image enhancement based on deep learning. UIEC^2-Net is anend-to-end trainable network, consisting of three blocks as follow: a RGBpixel-level block implements fundamental operations such as denoising andremoving color cast, a HSV global-adjust block for globally adjustingunderwater image luminance, color and saturation by adopting a novel neuralcurve layer, and an attention map block for combining the advantages of RGB andHSV block output images by distributing weight to each pixel. Experimentalresults on synthetic and real-world underwater images show the good performanceof our proposed method in both subjective comparisons and objective metrics.The code are available at https://github.com/BIGWangYuDong/UWEnhancement.

UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space