Low-light Image Enhancement
CVPR, pp.1777-1786, (2020)
We proposed a deep network for low-light image enhancement
Cited by3BibtexViews146Links
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Wenhan Yang,Shiqi Wang, Yuming Fang, Yue Wang,Jiaying Liu
CVPR, pp.3060-3069, (2020)
We aim to create a novel semi-supervised learning method utilizing the knowledge of synthetic paired low/normal-light images and unpaired high-quality data for low-light image enhancement
Cited by1BibtexViews131Links
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CVPR, pp.2278-2287, (2020)
We have presented a network with the proposed Attention to Context Encoding module for adaptively enhancing the high and low frequency layers, and Cross Domain Transformation module for noise suppression and detail enhancement
Cited by0BibtexViews65Links
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CVPR, pp.2755-2764, (2020)
We have presented a physics-based noise formation model together with a noise parameter calibration method to help resolve the difficulty of extreme low-light denoising
Cited by0BibtexViews82Links
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Zibo Meng, Runsheng Xu, Chiu Man Ho
It is extremely challenging to acquire perceptually plausible images under low-light conditions due to low SNR. Most recently, U-Nets have shown promising results for low-light imaging. However, vanilla U-Nets generate images with artifacts such as color inconsistency due to th...
Cited by0BibtexViews13Links
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Dokyeong Kwon, Guisik Kim,Junseok Kwon
BMVC, (2020)
Experimental results demonstrate that our method outperforms state-of-the-art methods
Cited by0BibtexViews69Links
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Jinxiu Liang, Yong Xu,Yuhui Quan, Jingwen Wang,Haibin Ling,Hui Ji
Motivated by the inherently coupled relationship between illumination and measurement noise, we proposed a novel deep bilateral Retinex method, which performs Retinex decomposition in the bilateral space of lowlight images
Cited by0BibtexViews47Links
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Zhang Cheng,Yan Qingsen, zhu Yu, Li Xianjun,Sun Jinqiu,Zhang Yanning
ICME, pp.1-6, (2020)
We propose a novel Attention-based Low-light image Enhancement Network which directly converts raw image to color image
Cited by0BibtexViews89Links
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Wang Keqi,Gao Peng,Hoi Steven, Guo Qian, Qian Yuhua
A novel network architecture has been proposed for processing extreme low-light images and correspondingly the loss function has been modified for our task and the raw illumination map estimation function is designed to preserve high dynamic range in low-light environment
Cited by0BibtexViews84Links
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Li Xiaoxiao, Guo Xiaopeng, Mei Liye, Shang Mingyu, Gao Jie, Shu Maojing, Wang Xiang
We proposed the VP model to simulate the relationship between light source and H UMAN vision system, aiming at quantifying the visual perception information of images
Cited by0BibtexViews28Links
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arxiv, (2020)
We conduct experiments on three real-world datasets and show that our model outperforms the state-of-the-art models with respect to both contrast enhancement and image denoising
Cited by0BibtexViews99Links
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Fu Qingxu, Di Xiaoguang, Zhang Yu
IET Image Process., no. 14 (2020): 3433-3443
Our model has the smallest noise variance on the Noise Level Estimation metric compared to BM3D-based low-light methods and obtains near-optimal scores on the other three indices
Cited by0BibtexViews28Links
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Karadeniz Ahmet Serdar,Erdem Erkut, Erdem Aykut
We propose a coarse-to-fine network architecture which allows for simultaneous processing of a burst of raw dark images as input to obtain a high quality RGB image
Cited by0BibtexViews41Links
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Zhang Yu, Di Xiaoguang,Zhang Bin, Wang Chunhui
We propose a maximum entropy based Retinex model and a self-supervised image enhancement network
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CVPR, pp.6849-6857, (2019)
This paper presents a new neural network for enhancing underexposed photos
Cited by62BibtexViews61Links
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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.1-1, (2019)
Camera sensors often fail to capture clear images or videos in a poorly-lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the globa...
Cited by32BibtexViews96Links
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CoRR, (2019)
We address the low-light enhancement problem with a novel and flexible unsupervised framework
Cited by15BibtexViews132Links
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Yonghua Zhang,Jiawan Zhang,Xiaojie Guo
Proceedings of the 27th ACM International Conference on Multimedia, (2019): 1632-1640
Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple types of degradations, such as noise and color distortion due to the limited quality of cameras, hide in the dark. In other words, solely turning u...
Cited by11BibtexViews43Links
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Sig. Proc.: Image Comm., (2019): 175-190
We modeled low-light image enhancement as a distribution of localized enhancement functions using Gaussian Process trained at runtime with reference data generated from a CNN
Cited by8BibtexViews66Links
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Feifan Lv, Yu Li, Feng Lu
arXiv preprint arXiv:1908.00682, (2019)
Extensive experiments demonstrate that our solution outperforms state-of-the-art methods by a large margin
Cited by4BibtexViews23Links
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Keywords
LightingAtmospheric ModelingConvolutional Neural NetworkConvolutional Neural NetworksData-driven MethodsDeep LearningDynamic RangeEstimationHeuristic AlgorithmsHistograms
Authors
Jiaying Liu
Paper 3
Wenhan Yang
Paper 3
Haibin Ling
Paper 2
Xiaojie Guo
Paper 2
Ding Liu
Paper 2
Feifan Lv
Paper 2
Wenjing Wang
Paper 2
Chen Wei
Paper 2
Xiaohui Shen
Paper 2
Erkut Erdem
Paper 1