A novel underwater image restoration method based on decomposition network and physical imaging model

2021 ◽  
Author(s):  
Yanfang Cui ◽  
Yujuan Sun ◽  
Muwei Jian ◽  
Xiaofeng Zhang ◽  
Tao Yao ◽  
...  
Keyword(s):  
Image Restoration ◽  
Imaging Model ◽  
Underwater Image ◽  
Restoration Method

scholarly journals Underwater Image Restoration Based on a Parallel Convolutional Neural Network

2019 ◽  
Vol 11 (13) ◽  
pp. 1591 ◽  
Author(s):  
Keyan Wang ◽  
Yan Hu ◽  
Jun Chen ◽  
Xianyun Wu ◽  
Xi Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Restoration ◽  
Ambient Light ◽  
Multi Scale ◽  
Imaging Model ◽  
Underwater Image ◽  
Ill Posed ◽  
Edge Features ◽  
Transmission Estimation

Restoring degraded underwater images is a challenging ill-posed problem. The existing prior-based approaches have limited performance in many situations due to the reliance on handcrafted features. In this paper, we propose an effective convolutional neural network (CNN) for underwater image restoration. The proposed network consists of two paralleled branches: a transmission estimation network (T-network) and a global ambient light estimation network (A-network); in particular, the T-network employs cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. The estimates produced by these two branches are leveraged to restore the clear image according to the underwater optical imaging model. Moreover, we develop a new underwater image synthesizing method for building the training datasets, which can simulate images captured in various underwater environments. Experimental results based on synthetic and real images demonstrate that our restored underwater images exhibit more natural color correction and better visibility improvement against several state-of-the-art methods.

Underwater Image Restoration Method Based on Scene Depth Estimation and White Balance

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
Keyword(s):  
Image Restoration ◽  
Depth Estimation ◽  
White Balance ◽  
Underwater Image ◽  
Restoration Method ◽  
Scene Depth

A fourth order P_Laplace underwater image restoration method based on MSG

2014 ◽  
Author(s):  
Tingting Ji ◽  
Guoyu Wang ◽  
Xiaolong Cheng ◽  
Guangrong Ji ◽  
Tianhong Ya
Keyword(s):  
Image Restoration ◽  
Fourth Order ◽  
Underwater Image ◽  
Restoration Method

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.

scholarly journals Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 595
Author(s):  
Huajun Song ◽  
Rui Wang
Keyword(s):  
Image Enhancement ◽  
Poor Performance ◽  
Weight Coefficient ◽  
Color Contrast ◽  
Dual Model ◽  
Spatial Cues ◽  
Multi Scale ◽  
Imaging Model ◽  
Underwater Image ◽  
Enhancement Method

Aimed at the two problems of color deviation and poor visibility of the underwater image, this paper proposes an underwater image enhancement method based on the multi-scale fusion and global stretching of dual-model (MFGS), which does not rely on the underwater optical imaging model. The proposed method consists of three stages: Compared with other color correction algorithms, white-balancing can effectively eliminate the undesirable color deviation caused by medium attenuation, so it is selected to correct the color deviation in the first stage. Then, aimed at the problem of the poor performance of the saliency weight map in the traditional fusion processing, this paper proposed an updated strategy of saliency weight coefficient combining contrast and spatial cues to achieve high-quality fusion. Finally, by analyzing the characteristics of the results of the above steps, it is found that the brightness and clarity need to be further improved. The global stretching of the full channel in the red, green, blue (RGB) model is applied to enhance the color contrast, and the selective stretching of the L channel in the Commission International Eclairage-Lab (CIE-Lab) model is implemented to achieve a better de-hazing effect. Quantitative and qualitative assessments on the underwater image enhancement benchmark dataset (UIEBD) show that the enhanced images of the proposed approach achieve significant and sufficient improvements in color and visibility.

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