Results 51 to 60 of about 22,853,467 (283)
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement
Tsinghua Science and Technology, 2023 Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement.
Yonglong Jiang +4 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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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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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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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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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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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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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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Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain
, 2023 With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios.
Xupei Zhang +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 +3 more
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