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.

近年来，由于海洋资源开发的兴起，水下图像增强已经引起了广泛的关注。受益于卷积神经网络（CNN）强大的表示能力，过去几年中已经提出了多种基于CNN的水下图像增强算法。但是，几乎所有这些算法都采用RGB颜色空间设置，该设置对图像属性（例如亮度和饱和度）不敏感。为了解决这个问题，我们提出了使用2种色彩空间（UICE ^ 2-Net）的水下图像增强卷积神经网络，该网络可以有效地将RGB色彩空间和HSV色彩空间整合到一个CNN中。据我们所知，该方法是第一个将HSV颜色空间用于基于深度学习的水下图像增强的方法。UIEC ^ 2-Net是一个端到端可训练网络，由以下三个块组成：RGB像素级块实现诸如降噪和消除色偏等基本操作，HSV全局调整块用于全局调整水下图像通过采用新型神经曲线层实现亮度，色彩和饱和度，以及通过将权重分配给每个像素来结合RGB和HSV块输出图像优点的注意图块。在合成和真实世界水下图像上的实验结果表明，该方法在主观比较和客观指标方面均具有良好的性能。该代码位于https://github.com/BIGWangYuDong/UWEnhancement。HSV全局调整块用于通过采用新型神经曲线层来全局调整水下图像的亮度，颜色和饱和度；以及注意图块，用于通过将权重分配给每个像素来结合RGB和HSV块输出图像的优点。在合成和真实世界水下图像上的实验结果表明，该方法在主观比较和客观指标方面均具有良好的性能。该代码位于https://github.com/BIGWangYuDong/UWEnhancement。HSV全局调整块用于通过采用新型神经曲线层来全局调整水下图像的亮度，颜色和饱和度；以及注意图块，用于通过将权重分配给每个像素来结合RGB和HSV块输出图像的优点。在合成和真实世界水下图像上的实验结果表明，该方法在主观比较和客观指标方面均具有良好的性能。该代码位于https://github.com/BIGWangYuDong/UWEnhancement。在合成和真实世界水下图像上的实验结果表明，该方法在主观比较和客观指标方面均具有良好的性能。该代码位于https://github.com/BIGWangYuDong/UWEnhancement。在合成和真实世界水下图像上的实验结果表明，该方法在主观比较和客观指标方面均具有良好的性能。该代码位于https://github.com/BIGWangYuDong/UWEnhancement。