Low Light Image Enhancement With Adaptive Light Initialization

LIU Bo; TIAN Guangliang; XIAO Bin; MA Jianfeng; BI Xiuli

doi:10.11999/JEIT230056

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 643-651

LIU Bo, TIAN Guangliang, XIAO Bin, MA Jianfeng, BI Xiuli. Low Light Image Enhancement With Adaptive Light Initialization[J]. Journal of Electronics & Information Technology, 2024, 46(2): 643-651. doi: 10.11999/JEIT230056

Citation:

LIU Bo, TIAN Guangliang, XIAO Bin, MA Jianfeng, BI Xiuli. Low Light Image Enhancement With Adaptive Light Initialization[J]. Journal of Electronics & Information Technology, 2024, 46(2): 643-651. doi: 10.11999/JEIT230056

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Low Light Image Enhancement With Adaptive Light Initialization

doi: 10.11999/JEIT230056

1.
Key Laboratory of Image Cognition, Chongqing Universtity of Posts and Telecommunications, Chongqing 400065, China
2.
School of Cyber Engineering, Xidian University, Xi’an 710071, China

Funds: The Natural Science Foundation of Chongqing for Distinguished Young Scholars (CSTB2022NSCQ-JQX0001), The National Natural Science Foundation of China (62172067, 61976031), Science and Technology Research Project of Chongqing Municipal Education Commission (KJQN202200635)

Received Date: 2023-02-15
Rev Recd Date: 2023-08-17

Available Online: 2023-08-24

Publish Date: 2024-02-29

Abstract

Abstract

Due to the high uncertainty in the estimation of the light component decomposition, how to accurately estimate the light component of an image has been a challenge to be addressed by image enhancement methods based on the Retinex model. An effective method is proposed to accurately estimate the initial illumination component in this paper. Specifically, the corresponding illumination weight matrices for different inputs are obtained to guide the adaptive initialization estimation, subsequently the estimation of the initial illumination components are optimized under the constraints of the illumination structure, and the non-linear illumination adjustment be performed on them. Finally, the Retinex be combined to obtain the enhanced images. Experiments show that our method not only achieves accurate image decomposition estimation, but also performs better in terms of both subjective visual effects and objective evaluation metrics on multiple datasets while maintaining good operational efficiency compared with existing methods for low-light image enhancement.
- Low-light image enhancement,
- Retinex,
- Adaptive estimation of illumination

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References(26)

References

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