Image Enhancement Method Based on Dark Channel Prior

Li, Dan; Shi, Hanqin; Wang, Hongdong; Liu, Weiwei; Wang, Likai

doi:10.1109/icceai55464.2022.00050

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) 2022

DOI: 10.1109/icceai55464.2022.00050

|View full text |Cite

Image Enhancement Method Based on Dark Channel Prior

Dan Li

Hanqin Shi

Hongdong Wang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network

Zhang,

Han,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Most of the existing low-light enhancement research focuses on global illumination enhancement while ignoring the issues of brightness unevenness and color distortion. To address this dilemma, we propose a low-light image enhancement method that can achieve good performance in luminance adjustment and color correction simultaneously. Specifically, the Luminance Adjustment Module is designed to model the global luminance adjustment parameters while taking into account the relationship between global and local illumination features, in order to prevent overexposure or underexposure. Furthermore, we design a Color Correction Module based on the attention mechanism, which utilizes the attention mechanism to capture global color features and correct the color deviation in the illumination-enhanced image. Additionally, we design a color loss function based on a 14-dimensional statistical feature vector related to color, enabling further restoration of the image’s true color. We conduct empirical studies on multiple public low-light datasets, demonstrating that the proposed method outperforms other representative state-of-the-art models regarding illumination enhancement and color correction.

show abstract

Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network

Zhang,

Han,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Image Enhancement Method Based on Dark Channel Prior

Cited by 1 publication

References 14 publications

Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network

Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network

Contact Info

Product

Resources

About