1 research outputs found
UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space
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
last 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 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 the good performance
of our proposed method in both subjective comparisons and objective metrics.
The code are available at https://github.com/BIGWangYuDong/UWEnhancement.Comment: 11 pages, 11 figure