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Convolutional Neural Network-Based Low Light Image Enhancement Method

Компьютерная оптика
With advances in science and technology, remote sensing images are vital for vegetation monitoring. The use of remote sensing allows for the collection of widespread, multi-temporal data on vegetation, leading to a better comprehension and management of ...
M.X. Li, C.J. Xu
doaj   +3 more sources

LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL

IEEE Access, 2022
Images captured in low-brightness environments often lead to poor visibility and exhibit artifacts such as low brightness, low contrast, and color distortion.
Atik Garg, Xin-Wen Pan, Lan-Rong Dung
doaj   +1 more source

Pyramid Diffusion Models for Low-light Image Enhancement

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
Recovering noise-covered details from low-light images is challenging, and the results given by previous methods leave room for improvement. Recent diffusion models show realistic and detailed image generation through a sequence of denoising refinements and motivate us to introduce them to low-light image enhancement for recovering realistic details ...
Dewei Zhou, Zongxin Yang, Yi Yang 0001
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Low Light Image Enhancement for Dark Images [PDF]

International Journal of Data Science and Analysis, 2020
Image plays an important role in this present technological world and leads to progress in multimedia communication, various research fields related to image processing, etc. Low-light image enhancement specifically addresses images captured in low-light conditions such as nighttime, where the common goal is to brighten and improve the contrast of the ...
Akshay Patil, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, Rupali Bora   +4 more
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MAGAN: Unsupervised Low-Light Image Enhancement Guided by Mixed-Attention

Big Data Mining and Analytics, 2022
Most learning-based low-light image enhancement methods typically suffer from two problems. First, they require a large amount of paired data for training, which are difficult to acquire in most cases.
Renjun Wang, Bin Jiang, Chao Yang, Qiao Li, Bolin Zhang   +4 more
doaj   +1 more source

Semantically Contrastive Learning for Low-Light Image Enhancement

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to the intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images and high-level semantic guidance, can improve the performance of ...
Dong Liang 0008, Ling Li 0010, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou 0001   +7 more
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PESiT: Progressive Joint Enhancement and Blind Super-Resolution for Low-Light and Low-Resolution Images Under Total Variation Constraints

IEEE Access
Traditional enhancement techniques can improve the contrast of low-light and low-resolution images, but they fail to recover their resolution. Conversely, traditional super-resolution (SR) algorithms can enhance resolution but not restore contrast.
He Deng, Kai Cheng, Yuqing Li
doaj   +1 more source

Low-Illumination Image Enhancement Based on Deep Learning Techniques: A Brief Review

Photonics, 2023
As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination.
Hao Tang, Hongyu Zhu, Linfeng Fei, Tingwei Wang, Yichao Cao, Chao Xie   +5 more
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Invertible network for unpaired low-light image enhancement

The Visual Computer, 2023
Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately. However, such data-driven models ignore the inherent characteristics of transformation between the low and normal light images, leading to unstable training and ...
Jize Zhang, Haolin Wang 0004, Xiaohe Wu, Wangmeng Zuo   +3 more
openaire   +3 more sources

DCTE-LLIE: A Dual Color-and-Texture-Enhancement-Based Method for Low-Light Image Enhancement

Computers
The enhancement of images captured under low-light conditions plays a vitally important role in the area of image processing and can significantly affect the performance of following operations.
Hua Wang, Jianzhong Cao, Lei Yang, Jijiang Huang   +3 more
doaj   +1 more source