Improved Image Quality of Hybrid Underwater Image Restoration (IR) Using the Dark Channel Prior (DCP), Color Attribution Prior, and Contrast Stretching

2019 ◽  
pp. 1077-1091
Author(s):  
Anuradha Vashishtha ◽  
Jamvant Singh Kumare
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Dark Channel Prior ◽  
Underwater Image ◽  
Dark Channel ◽  
Contrast Stretching

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.

Underwater Image Restoration Based on Background Light Estimation and Dark Channel Prior

2018 ◽  
Vol 38 (1) ◽  
pp. 0101002 ◽  
Author(s):  
谢昊伶 Xie Haoling ◽  
彭国华 Peng Guohua ◽  
王凡 Wang Fan ◽  
杨成 Yang Cheng
Keyword(s):  
Image Restoration ◽  
Dark Channel Prior ◽  
Background Light ◽  
Underwater Image ◽  
Dark Channel

scholarly journals Image Dehazing Based on Physical Model and Gray Projection

2019 ◽  
Vol 9 (1S3) ◽  
pp. 241-245
Keyword(s):  
Image Restoration ◽  
Physical Model ◽  
Dark Channel Prior ◽  
Image Dehazing ◽  
Dark Channel ◽  
Restoration Techniques ◽  
Image Computation ◽  
Computing Speed ◽  
Atmospheric Light

The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various works have been done to improve the detectable quality of picture taken under horrible climate. The images that are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to improve the computing speed. This computation can restore image with incredible quality and the speed of image computation is high. The proposed strategy is differentiated with other image enhancement strategies and image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images within less time.

A novel dark channel prior guided variational framework for underwater image restoration

2020 ◽  
Vol 66 ◽  
pp. 102732 ◽  
Author(s):  
Guojia Hou ◽  
Jingming Li ◽  
Guodong Wang ◽  
Huan Yang ◽  
Baoxiang Huang ◽  
...  
Keyword(s):  
Image Restoration ◽  
Dark Channel Prior ◽  
Underwater Image ◽  
Variational Framework ◽  
Dark Channel

Depth Estimation Based Underwater Image Enhancement

2014 ◽  
Vol 926-930 ◽  
pp. 1704-1707
Author(s):  
Qiu Yun Wang
Keyword(s):  
Image Enhancement ◽  
Image Data ◽  
Depth Estimation ◽  
Dark Channel Prior ◽  
Prior Model ◽  
Color Distortion ◽  
Underwater Image ◽  
Good Ability ◽  
Dark Channel

According to the image formation model and the nature of underwater images, we find that the effect of the haze and the color distortion seriously pollute the underwater image data, lowing the quality of the underwater images in the visibility and the quality of the data. Hence, aiming to reduce the noise and the haze effect existing in the underwater image and compensate the color distortion, the dark channel prior model is used to enhance the underwater image. We compare the dark channel prior model based image enhancement method to the contrast stretching based method for image enhancement. The experimental results proved that the dark channel prior model has good ability for processing the underwater images. The super performance of the proposed method is demonstrated as well.

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