Al Sobbahi, R.[Rayan],
Tekli, J.[Joe],
Low-Light Homomorphic Filtering Network for integrating image
enhancement and classification,
SP:IC(100), 2022, pp. 116527.
Elsevier DOI
2112
Image enhancement, Low-light conditions, Deep learning,
Object classification, Homomorphic filtering
BibRef
Al Sobbahi, R.[Rayan],
Tekli, J.[Joe],
Comparing deep learning models for low-light natural scene image
enhancement and their impact on object detection and classification:
Overview, empirical evaluation, and challenges,
SP:IC(109), 2022, pp. 116848.
Elsevier DOI
2210
BibRef
Earlier:
Low-Light Image Enhancement Using Image-to-Frequency Filter Learning,
CIAP22(II:693-705).
Springer DOI
2205
Image enhancement, Low-light conditions, Deep learning models,
Object detection and classification, Empirical comparison
BibRef
Zhang, Z.[Zenan],
Guo, J.[Jichang],
Yue, H.H.[Hui-Hui],
Wang, Y.D.[Yu-Dong],
Global guidance-based integration network for salient object
detection in low-light images,
JVCIR(95), 2023, pp. 103862.
Elsevier DOI
2309
Low-light images, Salient object detection,
Global information flow,
U-shaped attention refinement
BibRef
Lin, C.Y.[Chun-Yi],
Haq, M.A.[Muhamad Amirul],
Chen, J.H.[Jiun-Han],
Ruan, S.J.[Shanq-Jang],
Naroska, E.[Edwin],
Efficient Saliency Map Detection for Low-Light Images Based on Image
Gradient,
CirSysVideo(34), No. 2, February 2024, pp. 852-865.
IEEE DOI
2402
Image enhancement, Deep learning, Object detection,
Neural networks, Lighting, Histograms, Saliency detection,
saliency map detection
BibRef
Singh, K.[Kavinder],
Parihar, A.S.[Anil Singh],
MRN-LOD: Multi-exposure Refinement Network for Low-light Object
Detection,
JVCIR(99), 2024, pp. 104079.
Elsevier DOI
2403
Object detection, Multi-exposure images,
Adaptive refinement network, Low-light images, Feature extraction
BibRef
Yu, N.[Nana],
Wang, J.[Jie],
Shi, H.[Hong],
Zhang, Z.[Zihao],
Han, Y.[Yahong],
Degradation-removed multiscale fusion for low-light salient object
detection,
PR(155), 2024, pp. 110650.
Elsevier DOI Code:
WWW Link.
2408
Salient object detection, Low-light, Image enhancement,
Retinex decomposition, Multiscale adaptive fusion
BibRef
Lu, X.[Xiao],
Yuan, Y.L.[Yu-Lin],
Liu, X.[Xing],
Wang, L.[Lucai],
Zhou, X.Y.[Xuan-Yu],
Yang, Y.M.[Yi-Min],
Low-Light Salient Object Detection by Learning to Highlight the
Foreground Objects,
CirSysVideo(34), No. 8, August 2024, pp. 7712-7724.
IEEE DOI Code:
WWW Link.
2408
Object detection, Task analysis, Image enhancement, Lighting,
Feature extraction, Training, Dynamic range, datasets
BibRef
Xue, R.[Rui],
Duan, J.[Jialu],
Du, Z.W.[Zheng-Wei],
MPE-DETR: A multiscale pyramid enhancement network for object
detection in low-light images,
IVC(150), 2024, pp. 105202.
Elsevier DOI Code:
WWW Link.
2409
Object detection, Low-light images, Multiscale pyramid networks
BibRef
Zhang, H.[Han],
Wang, Y.F.[Yong-Fang],
Yang, Y.J.[Ying-Jie],
LL-WSOD: Weakly supervised object detection in low-light,
JVCIR(98), 2024, pp. 104010.
Elsevier DOI
2402
Weakly supervised learning, Object detection, Low light, Salient priors
BibRef
Jing, L.H.[Liang-Hu],
Wang, B.[Bo],
EMNet: Edge-guided multi-level network for salient object detection
in low-light images,
IVC(143), 2024, pp. 104933.
Elsevier DOI
2403
Low-light images, Salient object detection,
Multi-layer feature fusion, Edge feature highlight
BibRef
Zhao, D.[Dewei],
Shao, F.[Faming],
Zhang, S.[Sheng],
Yang, L.[Li],
Zhang, H.[Heng],
Liu, S.D.[Shao-Dong],
Liu, Q.[Qiang],
Advanced Object Detection in Low-Light Conditions:
Enhancements to YOLOv7 Framework,
RS(16), No. 23, 2024, pp. 4493.
DOI Link
2501
BibRef
Fu, C.P.[Chen-Ping],
Xiao, J.[Jiewen],
Yuan, W.Q.[Wan-Qi],
Liu, R.S.[Ri-Sheng],
Fan, X.[Xin],
Learning Cruxes to Push for Object Detection in Low-Quality Images,
CirSysVideo(34), No. 12, December 2024, pp. 12233-12243.
IEEE DOI Code:
WWW Link.
2501
Detectors, Feature extraction, Convolution, Accuracy, Training, Rain,
Degradation, Object detection, low-quality scenes, image enhancement
BibRef
Ye, S.[Shu],
Huang, W.X.[Wen-Xin],
Liu, W.X.[Wen-Xuan],
Chen, L.[Liang],
Wang, X.[Xiao],
Zhong, X.[Xian],
YES: You should Examine Suspect cues for low-light object detection,
CVIU(251), 2025, pp. 104271.
Elsevier DOI Code:
WWW Link.
2501
Image signal processing, Feature disentanglement,
Uneven lighting, Object-background cheating, Low-light object detection
BibRef
Zhang, H.[Hao],
Tang, L.F.[Lin-Feng],
Xiang, X.Y.[Xin-Yu],
Zuo, X.[Xuhui],
Ma, J.Y.[Jia-Yi],
Dispel Darkness for Better Fusion: A Controllable Visual Enhancer
Based on Cross-Modal Conditional Adversarial Learning,
CVPR24(26477-26486)
IEEE DOI Code:
WWW Link.
2410
Visualization, Codes, Image color analysis, Semantic segmentation,
Brightness, Object detection, Low-light, Enhancement, Image fusion,
Infrared
BibRef
Hashmi, K.A.[Khurram Azeem],
Kallempudi, G.[Goutham],
Stricker, D.[Didier],
Afzal, M.Z.[Muhammamd Zeshan],
FeatEnHancer: Enhancing Hierarchical Features for Object Detection
and Beyond Under Low-Light Vision,
ICCV23(6702-6712)
IEEE DOI
2401
BibRef
Duan, B.J.[Bing-Jie],
Wang, C.[Chao],
Li, Y.Y.[Yan-Yun],
NonReference Mapping Net,
ICIVC22(718-724)
IEEE DOI
2301
Training, Image quality, Visualization, Image color analysis,
Nonlinear distortion, Object detection, Task analysis,
NonReference mapping
BibRef
Liu, B.[Bokun],
Wei, J.Y.[Jun-Yu],
Su, S.J.[Shao-Jing],
Tong, X.Z.[Xiao-Zhong],
Research on Task-Driven Dual-Light Image Fusion and Enhancement
Method under Low Illumination,
ICIVC22(523-530)
IEEE DOI
2301
Visualization, Roads, Semantics, Lighting, Object detection,
Propagation losses, Reliability, image fusion and enhancement,
low illumination
BibRef
Chen, Z.L.[Zi-Long],
Liang, Y.L.[Ya-Ling],
Du, M.H.[Ming-Hui],
Attention-based Broad Self-guided Network for Low-light Image
Enhancement,
ICPR22(31-38)
IEEE DOI
2212
Wavelet transforms, Runtime, Stacking, Object detection,
Benchmark testing, Feature extraction, Data mining
BibRef
Morawski, I.[Igor],
Chen, Y.A.[Yu-An],
Lin, Y.S.[Yu-Sheng],
Dangi, S.[Shusil],
He, K.[Kai],
Hsu, W.H.[Winston H.],
GenISP: Neural ISP for Low-Light Machine Cognition,
NTIRE22(629-638)
IEEE DOI
2210
Image sensors, Image color analysis, Pipelines, Detectors,
Object detection, Cameras, Cognition
BibRef
Guo, L.Q.[Lan-Qing],
Wan, R.J.[Ren-Jie],
Su, G.M.[Guan-Ming],
Kot, A.C.[Alex C.],
Wen, B.[Bihan],
Multi-Scale Feature Guided Low-Light Image Enhancement,
ICIP21(554-558)
IEEE DOI
2201
Visualization, Inverse problems, Lighting, Object detection,
Feature extraction, Generative adversarial networks, Low-Light,
Unsupervised Learning
BibRef
Guo, H.F.[Hai-Feng],
Lu, T.[Tong],
Wu, Y.[Yirui],
Dynamic Low-Light Image Enhancement for Object Detection via
End-to-End Training,
ICPR21(5611-5618)
IEEE DOI
2105
Training, Image quality, Lighting, Object detection,
Detectors, Low-Light Image Enhancement, Object Detection
BibRef
Loh, Y.P.[Yuen Peng],
Exploring the Contributions of Low-light Image Enhancement to
Network-based Object Detection,
MOI2QDN20(655-669).
Springer DOI
2103
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Remote Sensing Object Detection Applications .