2 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
An Underwater Image Enhancement Benchmark Dataset and Beyond
Underwater image enhancement has been attracting much attention due to its
significance in marine engineering and aquatic robotics. Numerous underwater
image enhancement algorithms have been proposed in the last few years. However,
these algorithms are mainly evaluated using either synthetic datasets or few
selected real-world images. It is thus unclear how these algorithms would
perform on images acquired in the wild and how we could gauge the progress in
the field. To bridge this gap, we present the first comprehensive perceptual
study and analysis of underwater image enhancement using large-scale real-world
images. In this paper, we construct an Underwater Image Enhancement Benchmark
(UIEB) including 950 real-world underwater images, 890 of which have the
corresponding reference images. We treat the rest 60 underwater images which
cannot obtain satisfactory reference images as challenging data. Using this
dataset, we conduct a comprehensive study of the state-of-the-art underwater
image enhancement algorithms qualitatively and quantitatively. In addition, we
propose an underwater image enhancement network (called Water-Net) trained on
this benchmark as a baseline, which indicates the generalization of the
proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark
evaluations and the proposed Water-Net demonstrate the performance and
limitations of state-of-the-art algorithms, which shed light on future research
in underwater image enhancement. The dataset and code are available at
https://li-chongyi.github.io/proj_benchmark.html.Comment: 14 page