Low-light image enhancement based on membership function and gamma correction

2021 ◽  
Author(s):  
Shouxin Liu ◽  
Wei Long ◽  
Yanyan Li ◽  
Hong Cheng
Keyword(s):  
Image Enhancement ◽  
Membership Function ◽  
Gamma Correction ◽  
Low Light ◽  
Light Image

scholarly journals Low‐light image enhancement based on Retinex decomposition and adaptive gamma correction

2020 ◽  
Author(s):  
Jingyu Yang ◽  
Yuwei Xu ◽  
Huanjing Yue ◽  
Zhongyu Jiang ◽  
Kun Li
Keyword(s):  
Image Enhancement ◽  
Gamma Correction ◽  
Low Light ◽  
Light Image ◽  
Adaptive Gamma Correction

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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