scholarly journals Underwater image restoration via depth map and illumination estimation based on single image

2021 ◽  
Author(s):  
Jingchun Zhou ◽  
Tongyu Yang ◽  
Wenqi Ren ◽  
Dan Zhang ◽  
Weishi Zhang
Keyword(s):  
Image Restoration ◽  
Depth Map ◽  
Single Image ◽  
Illumination Estimation ◽  
Underwater Image

scholarly journals Underwater Image Restoration via Non-Convex Non-Smooth Variation and Thermal Exchange Optimization

2021 ◽  
Vol 9 (6) ◽  
pp. 570
Author(s):  
Qingliang Jiao ◽  
Ming Liu ◽  
Pengyu Li ◽  
Liquan Dong ◽  
Mei Hui ◽  
...  
Keyword(s):  
Image Restoration ◽  
Qualitative And Quantitative Analysis ◽  
Formation Model ◽  
High Quality ◽  
Thermal Exchange ◽  
Qualitative And Quantitative ◽  
Smooth Variation ◽  
Underwater Image ◽  
Dark Channel

The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex, non-smooth variation and thermal exchange optimization is proposed. Firstly, the underwater dark channel prior is used to estimate the rough transmission map. Secondly, the rough transmission map is refined by the proposed adaptive non-convex non-smooth variation. Then, Thermal Exchange Optimization is applied to compensate for the red channel of underwater images. Finally, the restored image can be estimated via the image formation model. The results show that the proposed algorithm can output high-quality images, according to qualitative and quantitative analysis.

Single Image Restoration with Generative Priors

2021 ◽  
Author(s):  
Kalliopi Basioti ◽  
George V. Moustakides
Keyword(s):  
Image Restoration ◽  
Single Image

scholarly journals The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  
Keyword(s):  
Structural Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Image Synthesis ◽  
Map Estimation ◽  
Estimation Model ◽  
Depth Prediction ◽  
Underwater Image ◽  
Underwater Vision ◽  
Image Depth

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.

Single Underwater Image Restoration By Multi-Scale Fusion In Multi-Color Space

2021 ◽  
Author(s):  
Zhuang Zhou ◽  
Lili Wang ◽  
Binghua Su ◽  
Jialin Tang ◽  
Yaqing Feng ◽  
...  
Keyword(s):  
Image Restoration ◽  
Color Space ◽  
Multi Scale ◽  
Underwater Image
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