Multi-Feature Guided Low-Light Image Enhancement
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, recalibrat...
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doaj-5c967f780d2649b1835e359c4f7a90ec2021-06-01T01:37:47ZengMDPI AGApplied Sciences2076-34172021-05-01115055505510.3390/app11115055Multi-Feature Guided Low-Light Image EnhancementHong Liang0Ankang Yu1Mingwen Shao2Yuru Tian3School of Computer Science and Technology, China University of Petroleum Huadong, Qingdao 266580, ChinaSchool of Computer Science and Technology, China University of Petroleum Huadong, Qingdao 266580, ChinaSchool of Computer Science and Technology, China University of Petroleum Huadong, Qingdao 266580, ChinaSchool of Computer Science and Technology, China University of Petroleum Huadong, Qingdao 266580, ChinaDue 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.https://www.mdpi.com/2076-3417/11/11/5055low light enhancementfeature guideddenoise |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hong Liang Ankang Yu Mingwen Shao Yuru Tian |
spellingShingle |
Hong Liang Ankang Yu Mingwen Shao Yuru Tian Multi-Feature Guided Low-Light Image Enhancement Applied Sciences low light enhancement feature guided denoise |
author_facet |
Hong Liang Ankang Yu Mingwen Shao Yuru Tian |
author_sort |
Hong Liang |
title |
Multi-Feature Guided Low-Light Image Enhancement |
title_short |
Multi-Feature Guided Low-Light Image Enhancement |
title_full |
Multi-Feature Guided Low-Light Image Enhancement |
title_fullStr |
Multi-Feature Guided Low-Light Image Enhancement |
title_full_unstemmed |
Multi-Feature Guided Low-Light Image Enhancement |
title_sort |
multi-feature guided low-light image enhancement |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-05-01 |
description |
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. |
topic |
low light enhancement feature guided denoise |
url |
https://www.mdpi.com/2076-3417/11/11/5055 |
work_keys_str_mv |
AT hongliang multifeatureguidedlowlightimageenhancement AT ankangyu multifeatureguidedlowlightimageenhancement AT mingwenshao multifeatureguidedlowlightimageenhancement AT yurutian multifeatureguidedlowlightimageenhancement |
_version_ |
1721411896052023296 |