Underwater mapping 2022 - 2023
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Zefeng Zhao, Zhuang Zhou, Yunting Lai, Tenghui
Wang, Shujie Zou, Haohao Cai, and Haijun Xie
Clear underwater images are necessary for many
underwater applications, while absorption, scattering,
and different water conditions will lead to blurring and
different color deviations. This paper proposed a
fusion-based image enhancement method for various
water areas to overcome the limitations of the
available color correction and deblurring algorithms.
The authors proposed two novel image processing
methods, namely, an adaptive channel deblurring
method and a color correction method, by limiting the
histogram mapping interval.
Authors: Zheyong Li, Jinghua Li, Pei Zhang, Lihui
Zheng, Yilong Shen, Qi Li, Xin Li, and Tong Li
The detection of underwater targets through
hyperspectral imagery is a relatively novel topic as the
assumption of target background independence is no
longer valid, making it difficult to detect underwater
targets using land target information directly.
Meanwhile, deep-learning-based methods have faced
challenges regarding the availability of training
datasets, especially in underwater conditions. To solve
these problems, a transfer-based framework is
proposed in this paper, which exploits synthetic data to
train deep-learning models and transfers them to real-
Yuanheng Li, Shengxiong Yang, Yuehua Gong, Jingya
Cao, Guang Hu, Yutian Deng, Dongmei Tian, and
Unpaired Image-to-Image Translation using Cycle-
Consistent Adversarial Networks, also known as
CycleGAN, has been broadly studied regarding
underwater image enhancement, but it is difficult to
apply the model because it can be difficult to train if
the dataset used is not appropriate. In this article, the
authors devise a new method of building a dataset
and choosing the best images based on the widely
used Underwater Image Quality Measure (UIQM)
scheme to create the dataset.