Low-Light Image Enhancement by Refining Illumination Map with Self-Guided Filtering

2017 ◽  
Author(s):  
Zhuang Feng ◽  
Shijie Hao
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Light Image ◽  
Guided Filtering

Low light image enhancement method based on guided filtering

2021 ◽  
Author(s):  
Bin Yao ◽  
Zhen Han ◽  
Shiying Kang ◽  
Xuanying Wei ◽  
Lifeng He ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Light Image ◽  
Guided Filtering ◽  
Enhancement Method

Retinex based low-light image enhancement using guided filtering and variational framework

2018 ◽  
Vol 14 (2) ◽  
pp. 156-160 ◽  
Author(s):  
Shi Zhang ◽  
Gui-jin Tang ◽  
Xiao-hua Liu ◽  
Su-huai Luo ◽  
Da-dong Wang
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Light Image ◽  
Variational Framework ◽  
Guided Filtering

scholarly journals A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement

2021 ◽  
Author(s):  
Zhuqing Jiang ◽  
Haotian Li ◽  
Liangjie Liu ◽  
Aidong Men ◽  
Haiying Wang
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Light Image

scholarly journals Multi-Feature Guided Low-Light Image Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5055
Author(s):  
Hong Liang ◽  
Ankang Yu ◽  
Mingwen Shao ◽  
Yuru Tian
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Low Light ◽  
Low Contrast ◽  
Light Image ◽  
Affine Model ◽  
The Neural Network ◽  
Low Visibility ◽  
Model Robustness ◽  
Traditional Image

Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

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