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Journal articles on the topic 'Low light enhancement'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Hao, Shijie, Xu Han, Yanrong Guo, and Meng Wang. "Decoupled Low-Light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–19. http://dx.doi.org/10.1145/3498341.

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The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion, and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to sub-optimal results. In this article, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on improv

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SANTHIYA, S., S. NANDHINI, M. MOGANA PRIYA, and K. SELVA BHUVANESWARI. "LOW-LIGHT IMAGE ENHANCEMENT USING INVERTED ATMOSPHERIC LIGHT." i-manager’s Journal on Software Engineering 15, no. 4 (2021): 8. http://dx.doi.org/10.26634/jse.15.4.18142.

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Park, Seonhee, Kiyeon Kim, Soohwan Yu, and Joonki Paik. "Contrast Enhancement for Low-light Image Enhancement: A Survey." IEIE Transactions on Smart Processing & Computing 7, no. 1 (2018): 36–48. http://dx.doi.org/10.5573/ieiespc.2018.7.1.036.

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Liu, Kang, Zhihao Xv, Zhe Yang, Lian Liu, Xinyu Li, and Xiaopeng Hu. "Continuous detail enhancement framework for low-light image enhancement." Displays 88 (July 2025): 103040. https://doi.org/10.1016/j.displa.2025.103040.

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Dabas, Megha. "Low Light Image Enhancement Using Python." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem39588.

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ABSTRACT----The poor signal-to-noise ratio (SNR) in low-light photos frequently results in significant sensor noise. Moreover, the noise is non-Gaussian and signal-dependent. We propose a novel denoising technique to tackle the issue by combining weighted total variation (TV) regularization with a Poisson noise model. The weighted Total Variation (T V) regularization effectively eliminates noise while preserving details, whereas the Poisson noise model retains the nature of the noise. Our suggested strategy performs better on NIQE scores than the most advanced techniques. KEYWORDS----COOPERATI

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Journal, IJSREM, Dr S. Babu, Dr R. Rajmohan, et al. "MONOCHROME AUGMENTED LOW-LIGHT IMAGE ENHANCEMENT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem37853.

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Low light short exposure photography is challenging, but an important factor in capturing images in temporarily dynamic scenes avoiding unwanted effects such as ghosting, motion blur, camera shakes, image artifacts, etc. Monochrome augmented low-light image enhancement aims to get improved low-light short-exposure images by using an additional monochrome sensor and its data. Monochrome images typically possess a higher SNR (Signal-to-Noise Ratio) and better luma information, since it avoids the attenuation by the Bayer Filter. The objective here is to develop a deep learning based approach to

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Xie, Junyi, Hao Bian, Yuanhang Wu, Yu Zhao, Linmin Shan, and Shijie Hao. "Semantically-guided low-light image enhancement." Pattern Recognition Letters 138 (October 2020): 308–14. http://dx.doi.org/10.1016/j.patrec.2020.07.041.

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Zhou, Chu, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, and Boxin Shi. "Polarization-Aware Low-Light Image Enhancement." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3742–50. http://dx.doi.org/10.1609/aaai.v37i3.25486.

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Polarization-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 angle of polarization (AoP). Existing low-light image enhancement methods cannot handle the polarized images well since they operate in the intensity domain, without effectively exploiting the information provided by polarization. In this paper, we p

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Liang, Xiwen, and Xiaoyan Chen. "Enhancement methodology for low light image." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 12–19. http://dx.doi.org/10.5954/icarob.2023.ps3.

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Zhai, Guangtao, Wei Sun, Xiongkuo Min, and Jiantao Zhou. "Perceptual Quality Assessment of Low-light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3457905.

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Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low

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Gu, Wenjuan, Xin Li, Yuhanke Hu, Junxiang Peng, and Xiaobao Liu. "DT-Retinex: low-light enhancement network based on diffuse denoising and light enhancement." Digital Signal Processing 166 (November 2025): 105416. https://doi.org/10.1016/j.dsp.2025.105416.

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Liu, Weiqiang, Peng Zhao, Xiangying Song, and Bo Zhang. "A Survey of Low-light Image Enhancement." Frontiers in Computing and Intelligent Systems 1, no. 3 (2022): 88–92. http://dx.doi.org/10.54097/fcis.v1i3.2242.

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With the higher requirements of computer vision image enhancement of low-light image has become an important research content of computer vision. Traditional low-light image enhancement algorithms can improve image brightness and detailed visibility to varying degrees, but due to their strict mathematical derivation, such methods have bottlenecks and are difficult to break through their limits. With the development of deep learning and the birth of large-scale data sets, low-light image enhancement based on deep learning has become the mainstream trend. In this paper, first of all, the traditi

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KOJIMA, Seiichi, Noriaki SUETAKE, and Eiji UCHINO. "A Contrast Enhancement of Low-light Image Suppressing Over-enhancement." Japanese Journal of Ergonomics 56, Supplement (2020): 2B3–03–2B3–03. http://dx.doi.org/10.5100/jje.56.2b3-03.

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B., Mrs Rajeswari. "Night Time Image Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29951.

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Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to lear

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Liu, Lin, Junfeng An, Jianzhuang Liu, et al. "Low-Light Video Enhancement with Synthetic Event Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1692–700. http://dx.doi.org/10.1609/aaai.v37i2.25257.

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Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhancement, most LLVE methods utilize temporal information from adjacent frames to restore the color and remove the noise of the target frame. However, these algorithms, based on the framework of multi-frame alignment and enhancement, may produce multi-frame fusion artifacts when encountering extreme low light or fast motion. In this paper, inspired by the low latency and high dynamic range of events, we use synthetic events

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Zhou, Han, Wei Dong, Xiaohong Liu, Yulun Zhang, Guangtao Zhai, and Jun Chen. "Low-Light Image Enhancement via Generative Perceptual Priors." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10752–60. https://doi.org/10.1609/aaai.v39i10.33168.

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Although significant progress has been made in enhancing visibility, retrieving texture details, and mitigating noise in Low-Light (LL) images, the challenge persists in applying current Low-Light Image Enhancement (LLIE) methods to real-world scenarios, primarily due to the diverse illumination conditions encountered. Furthermore, the quest for generating enhancements that are visually realistic and attractive remains an underexplored realm. In response to these challenges, we present a novel LLIE framework with the guidance of Generative Perceptual Priors (GPP-LLIE) derived from vision-langu

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Liu, Jiaying, Dejia Xu, Wenhan Yang, Minhao Fan, and Haofeng Huang. "Benchmarking Low-Light Image Enhancement and Beyond." International Journal of Computer Vision 129, no. 4 (2021): 1153–84. http://dx.doi.org/10.1007/s11263-020-01418-8.

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18

Wang, Yufei, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, and Alex Kot. "Low-Light Image Enhancement with Normalizing Flow." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2604–12. http://dx.doi.org/10.1609/aaai.v36i3.20162.

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To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distributio

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Liang, Hong, Ankang Yu, Mingwen Shao, and Yuru Tian. "Multi-Feature Guided Low-Light Image Enhancement." Applied Sciences 11, no. 11 (2021): 5055. http://dx.doi.org/10.3390/app11115055.

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Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, a

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Huang, Haofeng, Wenhan Yang, Yueyu Hu, Jiaying Liu, and Ling-Yu Duan. "Towards Low Light Enhancement With RAW Images." IEEE Transactions on Image Processing 31 (2022): 1391–405. http://dx.doi.org/10.1109/tip.2022.3140610.

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Wang, Li-Wen, Zhi-Song Liu, Wan-Chi Siu, and Daniel P. K. Lun. "Lightening Network for Low-Light Image Enhancement." IEEE Transactions on Image Processing 29 (2020): 7984–96. http://dx.doi.org/10.1109/tip.2020.3008396.

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22

Ma, Long, Tengyu Ma, and Risheng Liu. "The review of low-light image enhancement." Journal of Image and Graphics 27, no. 5 (2022): 1392–409. http://dx.doi.org/10.11834/jig.210852.

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23

Li, Jinfeng. "Low-light image enhancement with contrast regularization." Frontiers in Computing and Intelligent Systems 1, no. 3 (2022): 25–28. http://dx.doi.org/10.54097/fcis.v1i3.2022.

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Because the processing of existing low-light images undergoes multiple sampling processing, there is serious information degradation, and only clear images are used as positive samples to guide network training, low-light image enhancement processing is still a challenging and unsettled problem. Therefore, a multi-scale contrast learning low-light image enhancement network is proposed. First, the image generates rich features through the input module, and then the features are imported into a multi-scale enhancement network with dense residual blocks, using positive and negative samples to gui

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24

Patil, Akshay, Tejas Chaudhari, Ketan Deo, Kalpesh Sonawane, and Rupali Bora. "Low Light Image Enhancement for Dark Images." International Journal of Data Science and Analysis 6, no. 4 (2020): 99. http://dx.doi.org/10.11648/j.ijdsa.20200604.11.

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Jin, Yutao, Xiaoyan Chen, and Xiwen Liang. "A lightweight low-light image enhancement network." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 808–12. http://dx.doi.org/10.5954/icarob.2023.os31-4.

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26

Patel, Kartik. "Low-Light Image Enhancement Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3390–96. https://doi.org/10.22214/ijraset.2025.68073.

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Low-light image enhancement is a critical task in computer vision, aimed at improving the visibility and perceptual quality of images captured under poor lighting conditions. Traditional methods often suffer from over-enhancement, noise amplification, and loss of fine details. In this paper, we propose a lightweight Convolutional Neural Network (CNN)-based model that leverages a novel loss function combining Mean Absolute Error (MAE) and Contrast Consistency Loss (CCL). Our method focuses on preserving contrast and structural details while minimizing computational overhead. Experiments conduct

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ZHANG, Jianqiang, and Qiusheng HE. "Context aware low-light image enhancement algorithm." Chinese Journal of Liquid Crystals and Displays 40, no. 5 (2025): 751–60. https://doi.org/10.37188/cjlcd.2024-0277.

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Wang, Yun-Fei, He-Ming Liu, and Zhao-Wang Fu. "Low-Light Image Enhancement via the Absorption Light Scattering Model." IEEE Transactions on Image Processing 28, no. 11 (2019): 5679–90. http://dx.doi.org/10.1109/tip.2019.2922106.

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29

Sun, Yanpeng, Zhanyou Chang, Yong Zhao, Zhengxu Hua, and Sirui Li. "Progressive Two-Stage Network for Low-Light Image Enhancement." Micromachines 12, no. 12 (2021): 1458. http://dx.doi.org/10.3390/mi12121458.

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At night, visual quality is reduced due to insufficient illumination so that it is difficult to conduct high-level visual tasks effectively. Existing image enhancement methods only focus on brightness improvement, however, improving image quality in low-light environments still remains a challenging task. In order to overcome the limitations of existing enhancement algorithms with insufficient enhancement, a progressive two-stage image enhancement network is proposed in this paper. The low-light image enhancement problem is innovatively divided into two stages. The first stage of the network e

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V.deepika, Nivedha C., Sai roshini P.S., and S. Arun Kumar Guide:. "Variance Reduction in Low Light Image Enhancement Model." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 4 (2020): 139–42. https://doi.org/10.35940/ijrte.D4723.119420.

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In image processing, enhancement of images taken in low light is considered to be a tricky and intricate process, especially for the images captured at nighttime. It is because various factors of the image such as contrast, sharpness and color coordination should be handled simultaneously and effectively. To reduce the blurs or noises on the low-light images, many papers have contributed by proposing different techniques. One such technique addresses this problem using a pipeline neural network. Due to some irregularity in the working of the pipeline neural networks model [1], a hidden layer i

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Wang, Hua, Jianzhong Cao, Lei Yang, and Jijiang Huang. "DCTE-LLIE: A Dual Color-and-Texture-Enhancement-Based Method for Low-Light Image Enhancement." Computers 13, no. 6 (2024): 134. http://dx.doi.org/10.3390/computers13060134.

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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. In recent years, deep learning techniques have been leveraged in the area of low-light image enhancement tasks, and deep-learning-based low-light image enhancement methods have been the mainstream for low-light image enhancement tasks. However, due to the inability of existing methods to effectively maintain the color distribution of the original input image and to effectively handle feature descriptio

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Shi, Yangming, Xiaopo Wu, and Ming Zhu. "Interactive and Fast Low-Light Image Enhancement Algo-rithm and Application." Journal of Physics: Conference Series 2258, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2258/1/012003.

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Abstract To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhan

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Jiang, Yonglong, Liangliang Li, Jiahe Zhu, Yuan Xue, and Hongbing Ma. "DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement." Tsinghua Science and Technology 28, no. 4 (2023): 743–53. http://dx.doi.org/10.26599/tst.2022.9010047.

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Li, Xiang, Zeyu Li, Lirong Zhou, and Zhao Huang. "FOLD: Low-Level Image Enhancement for Low-Light Object Detection Based on FPGA MPSoC." Electronics 13, no. 1 (2024): 230. http://dx.doi.org/10.3390/electronics13010230.

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Object detection has a wide range of applications as the most fundamental and challenging task in computer vision. However, the image quality problems such as low brightness, low contrast, and high noise in low-light scenes cause significant degradation of object detection performance. To address this, this paper focuses on object detection algorithms in low-light scenarios, carries out exploration and research from the aspects of low-light image enhancement and object detection, and proposes low-level image enhancement for low-light object detection based on the FPGA MPSoC method. On the one

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Rohima, Rohima, Wanayumini Wanayumini, and Rika Rosnelly. "ANALISIS PENGARUH LOW-LIGHT IMAGE ENHANCEMENT PADA PENGENALAN WAJAH." CSRID (Computer Science Research and Its Development Journal) 13, no. 2 (2021): 118. http://dx.doi.org/10.22303/csrid.13.2.2021.118-129.

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<p class="bodyabstract"><span lang="X-NONE">Sistem pengenalan wajah secara umum akan digunakan secara real time dalam mengenali individu, artinya noise tidak dapat terhindarkan. Salah satu masalah yang dianggap umum adalah kokndisi pencahayaan. Kondisi pencahayaan terjadi akibat pancaran yang diterima objek tidak mencukupi sehingga cenderung memiliki visibilat rendah, kontras berkurang, warna kabur, dan detail yang kabur. Maka low-light image enhancement dapat menjadi solusinya. Terdapat banyak sekali metode low-light image enhancement yang tersedia, namun mana teknik yang lebih ba

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Rasheed, Muhammad Tahir, Guiyu Guo, Daming Shi, Hufsa Khan, and Xiaochun Cheng. "An Empirical Study on Retinex Methods for Low-Light Image Enhancement." Remote Sensing 14, no. 18 (2022): 4608. http://dx.doi.org/10.3390/rs14184608.

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A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test dataset

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Lv, Feifan, Yu Li, and Feng Lu. "Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset." International Journal of Computer Vision 129, no. 7 (2021): 2175–93. http://dx.doi.org/10.1007/s11263-021-01466-8.

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Tian, Zhen, Peixin Qu, Jielin Li, et al. "A Survey of Deep Learning-Based Low-Light Image Enhancement." Sensors 23, no. 18 (2023): 7763. http://dx.doi.org/10.3390/s23187763.

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Images captured under poor lighting conditions often suffer from low brightness, low contrast, color distortion, and noise. The function of low-light image enhancement is to improve the visual effect of such images for subsequent processing. Recently, deep learning has been used more and more widely in image processing with the development of artificial intelligence technology, and we provide a comprehensive review of the field of low-light image enhancement in terms of network structure, training data, and evaluation metrics. In this paper, we systematically introduce low-light image enhancem

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E., Okorie, Iloka B. C., Okoh C. C., and Ejikeme A. "Low Light Vision Enhancement Using the Hazing Algorithm." International Journal of Research 10, no. 8 (2023): 167–81. https://doi.org/10.5281/zenodo.8224041.

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<strong>This journal publication presents a novel low light vision enhancement technique utilizing the Hazing algorithm. Low light conditions often pose significant challenges in various applications, such as surveillance, security, and outdoor imaging. The proposed technique aims to improve visibility and enhance the quality of low light images, thereby enabling better analysis and interpretation of visual information. The Hazing algorithm is a state-of-the-art method designed to address the limitations of traditional enhancement techniques in low light scenarios. It utilizes a combination of

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Yao, Zhuo. "Low-Light Image Enhancement and Target Detection Based on Deep Learning." Traitement du Signal 39, no. 4 (2022): 1213–20. http://dx.doi.org/10.18280/ts.390413.

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Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning

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Ming, Feng, Zhihui Wei, and Jun Zhang. "Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain." Applied Sciences 14, no. 1 (2023): 332. http://dx.doi.org/10.3390/app14010332.

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Low-light image enhancement is an important task in computer vision. Deep learning-based low-light image enhancement has made significant progress. But the current methods also face the challenge of relying on a wide variety of low-light/normal-light paired images and amplifying noise while enhancing brightness. Based on existing experimental observation that most luminance information concentrates on amplitudes while noise is closely related to phases, an unsupervised low-light image enhancement method in the Fourier transform domain is proposed. In our method, the low-light image is firstly

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Vishnu, Choundur. "Low Light Image Enhancement using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3463–72. http://dx.doi.org/10.22214/ijraset.2021.35787.

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Great quality images and pictures are remarkable for some perceptions. Nonetheless, not each and every images are in acceptable features and quality as they are capture in non-identical light atmosphere. At the point when an image is capture in a low light state the pixel esteems are in a low-esteem range, which will cause image quality to decrease evidently. Since the entire image shows up dull, it's difficult to recognize items or surfaces clearly. Thus, it is vital to improve the nature of low-light images. Low light image enhancement is required in numerous PC vision undertakings for objec

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Garg, Atik, Xin-Wen Pan, and Lan-Rong Dung. "LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL." IEEE Access 10 (2022): 33547–60. http://dx.doi.org/10.1109/access.2022.3161527.

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Jeon, Jong Ju, and Il Kyu Eom. "Low-light image enhancement using inverted image normalized by atmospheric light." Signal Processing 196 (July 2022): 108523. http://dx.doi.org/10.1016/j.sigpro.2022.108523.

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SHI, Jihao, Yuzhong ZHONG, Xiujuan ZHENG, and Songyi DIAN. "Low-light image enhancement algorithm based on light scattering attenuation model." Optics and Precision Engineering 31, no. 8 (2023): 1244–55. http://dx.doi.org/10.37188/ope.20233108.1244.

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YAN, Guanghui, Baijing WU, and Long MA. "LightDiffu DCE： low light image enhancement based on light intensity diffusion." Optics and Precision Engineering 33, no. 7 (2025): 1114–29. https://doi.org/10.37188/ope.20253307.1114.

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Li, Fei, Jiangbin Zheng, and Yuan‐fang Zhang. "Generative adversarial network for low‐light image enhancement." IET Image Processing 15, no. 7 (2021): 1542–52. http://dx.doi.org/10.1049/ipr2.12124.

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Liang, Dong, Ling Li, Mingqiang Wei, et al. "Semantically Contrastive Learning for Low-Light Image Enhancement." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1555–63. http://dx.doi.org/10.1609/aaai.v36i2.20046.

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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 cutting-edge LLE models? Here, we propose an effective semantically contrastive learning paradigm for LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image enhancement task as multi-task joint learning, where LLE is converted i

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Yu, Yabin. "Feature Fusion Network for Low-Light Image Enhancement." Journal of Physics: Conference Series 2010, no. 1 (2021): 012117. http://dx.doi.org/10.1088/1742-6596/2010/1/012117.

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Lu, Yucheng, Dong-Wook Kim, and Seung-Won Jung. "DeepSelfie: Single-Shot Low-Light Enhancement for Selfies." IEEE Access 8 (2020): 121424–36. http://dx.doi.org/10.1109/access.2020.3006525.

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