Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network

CHEN Yong; CHEN Dong; LIU Huanlin; HUANG Meiyong; WANG Bo

doi:10.11999/JEIT210386

Volume 44 Issue 6

Jun. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 2166-2174

CHEN Yong, CHEN Dong, LIU Huanlin, HUANG Meiyong, WANG Bo. Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2166-2174. doi: 10.11999/JEIT210386

Citation:

CHEN Yong, CHEN Dong, LIU Huanlin, HUANG Meiyong, WANG Bo. Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2166-2174. doi: 10.11999/JEIT210386

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Unreferenced Low-lighting Image Enhancement Based on Deep Convolutional Neural Network

doi: 10.11999/JEIT210386

1.
Key Laboratory of Industrial Internet of Things and Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (51977021), Chongqing Key Technology Innovation Project (cstc2019jscx-mbdX0004)

Received Date: 2021-05-07
Rev Recd Date: 2022-03-21

Available Online: 2022-03-24

Publish Date: 2022-06-21

Abstract

Abstract

To address the shortcomings of existing low illumination image enhancement algorithms in achieving detail enhancement while considering noise suppression, a reference-free low-illumination image enhancement method based on deep convolutional neural networks is proposed in the paper. First, the illumination and reflection components are extracted from the input low-illumination image based on Retinex theory and optimised separately, after which the optimised illumination and reflection components are multiplied to obtain the enhanced image. loss to update the network parameters, meanwhile, the denoising effect of Block Matching 3D (BM3D ) is integrated into the optimization process of reflection components. The experimental results show that the algorithm in this paper can effectively enhance the contrast and brightness of low-illumination images compared to existing mainstream algorithms, while maintaining the naturalness of the images.
- Image enhancement,
- Low light,
- Retinex,
- Convolutional Neural Network(CNN),
- Consistent trend

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References(22)

References

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