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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
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Enhancing Low-Light Images Using Infrared Encoded Images
2023 IEEE International Conference on Image Processing (ICIP), 2023 Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss, which limits the capacity of recovering the brightness, contrast, and texture details due to the small number of income photons.
Shulin Tian +5 more
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Convolutional Neural Network-Based Low Light Image Enhancement Method
Компьютерная оптикаWith 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
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Endoscopic image enhancement with noise suppression
Healthcare Technology Letters, 2018 Stereoscopic endoscopes have been used increasingly in minimally invasive surgery to visualise the organ surface and manipulate various surgical tools. However, insufficient and irregular light sources become major challenges for endoscopic surgery.
Wenyao Xia +2 more
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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 +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 +4 more
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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 +7 more
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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 +5 more
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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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IEEE AccessTraditional 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
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