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Low Light Enhancement by Unsupervised Network
2020 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2020This paper focuses on unsupervised low-light enhancement methods for practical applications such as automated driving, surveillance cameras, and so on. This paper proposes an unsupervised method that is independent of paired samples and works well to solve the image darkness problem.
Yangyang Qu, Yongsheng Ou, Rong Xiong
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Benchmarking Low-Light Image Enhancement and Beyond
International Journal of Computer Vision, 2021In this paper, we present a systematic review and evaluation of existing single-image low-light enhancement algorithms. Besides the commonly used low-level vision oriented evaluations, we additionally consider measuring machine vision performance in the low-light condition via face detection task to explore the potential of joint optimization of high ...
Jiaying Liu, Wenhan Yang, Dejia Xu
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Aesthetics-Guided Low-Light Enhancement
IEEE Transactions on Pattern Analysis and Machine IntelligenceEvaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into LLE a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training LLE models.
Dong Liang 0008 +5 more
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Lightening Network for Low-Light Image Enhancement
IEEE Transactions on Image Processing, 2020Low-light image enhancement is a challenging task that has attracted considerable attention. Pictures taken in low-light conditions often have bad visual quality. To address the problem, we regard the low-light enhancement as a residual learning problem that is to estimate the residual between low- and normal-light images.
Li-Wen Wang +3 more
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Polarization-Aware Low-Light Image Enhancement
Proceedings of the AAAI Conference on Artificial Intelligence, 2023Polarization-based vision algorithms have found uses in various applications since polarization provides additional physical constraints. However, in low-light conditions, their performance would be severely degenerated since the captured polarized images could be noisy, leading to noticeable degradation in the degree of polarization (DoP) and the ...
Chu Zhou +5 more
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Image enhancement for extremely low light conditions
2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2014In this paper, a novel methodology is proposed for contrast enhancement and noise reduction in very noisy data with low dynamic range on images captured by surveillance camera under extremely low light condition. For the initial noise reduction, a motion adaptive temporal filtering based on the Kalman filter is employed.
Dubok Park +4 more
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Semantically-guided low-light image enhancement
Pattern Recognition Letters, 2020Abstract Recently, extensive research efforts have been made on low-light image enhancement. Many novel models have been proposed, such as the ones based on the Retinex theory, multiple exposure fusion, and deep neural networks. However, current models do not directly consider the semantic information in the modeling process.
Junyi Xie +5 more
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A novel framework for enhancement of the low lighting video
2017 IEEE Symposium on Computers and Communications (ISCC), 2017A novel and effective framework for the enhancement of low lighting images is proposed in this paper. The novel framework presents an optimized de-haze algorithm on inverted images to enhance the low-dynamic-range images which optimizes the complicated process of computing the parameters A and t(x).
Jianhua Pang +2 more
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