Underwater image dehaze using scene depth estimation with adaptive color correction

Author(s):  
Xueyan Ding ◽  
Yafei Wang ◽  
Jun Zhang ◽  
Xianping Fu
2019 ◽  
Vol 56 (3) ◽  
pp. 031008
Author(s):  
蔡晨东 Cai Chendong ◽  
霍冠英 Huo Guanying ◽  
周妍 Zhou Yan ◽  
韩辉 Han Hui

2021 ◽  
Vol 91 ◽  
pp. 106981
Author(s):  
Weidong Zhang ◽  
Xipeng Pan ◽  
Xiwang Xie ◽  
Lingqiao Li ◽  
Zimin Wang ◽  
...  

2020 ◽  
Vol 10 (18) ◽  
pp. 6392
Author(s):  
Xieliu Yang ◽  
Chenyu Yin ◽  
Ziyu Zhang ◽  
Yupeng Li ◽  
Wenfeng Liang ◽  
...  

Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.


2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.


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