Raw underwater images are degraded due to wavelength dependent light
attenuation and scattering, limiting their applicability in vision systems.
Another factor that makes enhancing underwater images particularly challenging
is the diversity of the water types in which they are captured. For example,
images captured in deep oceanic waters have a different distribution from those
captured in shallow coastal waters. Such diversity makes it hard to train a
single model to enhance underwater images. In this work, we propose a novel
model which nicely handles the diversity of water during the enhancement, by
adversarially learning the content features of the images by disentangling the
unwanted nuisances corresponding to water types (viewed as different domains).
We use the learned domain agnostic features to generate enhanced underwater
images. We train our model on a dataset consisting images of 10 Jerlov water
types. Experimental results show that the proposed model not only outperforms
the previous methods in SSIM and PSNR scores for almost all Jerlov water types
but also generalizes well on real-world datasets. The performance of a
high-level vision task (object detection) also shows improvement using enhanced
images with our model.

本文介绍了一种用于增强不同水域下水下图像的模型，采用深度学习技术，通过对图像特征进行无关领域的学习，提高了图像增强的性能，并在物体检测任务中表现出了显著的改善。

使用领域对抗学习的全能水下图像增强

All-In-One Underwater Image Enhancement using Domain-Adversarial  Learning

Underwater images suffer from color distortion and low contrast, because
light is attenuated while it propagates through water. Attenuation under water
varies with wavelength, unlike terrestrial images where attenuation is assumed
to be spectrally uniform. The attenuation depends both on the water body and
the 3D structure of the scene, making color restoration difficult.
Unlike existing single underwater image enhancement techniques, our method
takes into account multiple spectral profiles of different water types. By
estimating just two additional global parameters: the attenuation ratios of the
blue-red and blue-green color channels, the problem is reduced to single image
dehazing, where all color channels have the same attenuation coefficients.
Since the water type is unknown, we evaluate different parameters out of an
existing library of water types. Each type leads to a different restored image
and the best result is automatically chosen based on color distribution.
We collected a dataset of images taken in different locations with varying
water properties, showing color charts in the scenes. Moreover, to obtain
ground truth, the 3D structure of the scene was calculated based on stereo
imaging. This dataset enables a quantitative evaluation of restoration
algorithms on natural images and shows the advantage of our method.

本文提出了一种针对水下图像色彩恢复问题的改进方法，通过考虑不同水类型的多个光谱特征并估计两个额外的全局参数，实现了单图像去雾处理。通过在包含不同水性质的场景中采集图像并计算 3D 结构进行量化评估，结果表明该方法具有较好的还原效果。