Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and...

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Vydáno v:	Photonics Ročník 11; číslo 2; s. 190
Hlavní autoři:	Guan, Lei, Dong, Jiawei, Li, Qianxi, Huang, Jijiang, Chen, Weining, Wang, Hao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.02.2024
Témata:	Algorithms Collaboration collaborative constraint dark light enhancement Datasets Deep learning fidelity term Illumination Image enhancement Image restoration Light Methods Modules Noise control Noise reduction Optimization Robustness self-encoding prior Semantics Teaching methods United States New Jersey
ISSN:	2304-6732, 2304-6732
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Abstract	The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
AbstractList	The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
Audience	Academic
Author	Guan, Lei Dong, Jiawei Huang, Jijiang Chen, Weining Li, Qianxi Wang, Hao
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SubjectTerms	Algorithms Collaboration collaborative constraint dark light enhancement Datasets Deep learning fidelity term Illumination Image enhancement Image restoration Light Methods Modules Noise control Noise reduction Optimization Robustness self-encoding prior Semantics Teaching methods
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Title	Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints
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