15.3.1.9.3 Fog, Fog Detection, Mist, Visibility, Driver Assistance

Evans, W.E.[William E.], Viezee, W.[William],
Visibility monitor employing television camera,
US_Patent4,216,498, Aug 5, 1980
WWW Link. BibRef 8008

Kwon, T.M.[Taek Mu],
Video camera-based visibility measurement system,
US_Patent6,853,453, Feb 8, 2005
WWW Link. BibRef 0502
And: US_Patent7,016,045, Mar 21, 2006
WWW Link. BibRef

Hautière, N.[Nicolas], Labayrade, R., Aubert, D.[Didier],
Real-Time Disparity Contrast Combination for Onboard Estimation of the Visibility Distance,
ITS(7), No. 2, June 2006, pp. 201-212.
IEEE DOI 0606
BibRef

Hautiere, N.[Nicolas], Labayrade, R.[Raphaël], Aubert, D.[Didier],
Estimation of the Visibility Distance by Stereovision: A Generic Approach,
IEICE(E89-D), No. 7, July 2006, pp. 2084-2091.
DOI Link 0607
BibRef

Hautière, N.[Nicolas], Aubert, D.[Didier],
Visible Edges Thresholding: a HVS based Approach,
ICPR06(II: 155-158).
IEEE DOI 0609
Is it visible to human eye? BibRef

Hautiére, N.[Nicolas], Tarel, J.P.[Jean-Philippe], Lavenant, J.[Jean], Aubert, D.[Didier],
Automatic fog detection and estimation of visibility distance through use of an onboard camera,
MVA(17), No. 1, April 2006, pp. 8-20.
Springer DOI 0604
BibRef

Hautiere, N.[Nicolas], Tarel, J.P.[Jean-Philippe], Aubert, D.[Didier],
Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Hautiere, N., Labayrade, R., Perrollaz, M., Aubert, D.,
Road Scene Analysis by Stereovision: a Robust and Quasi-Dense Approach,
ICARCV06(1-6).
IEEE DOI 0612
BibRef

Hautiere, N.[Nicolas], Boubezoul, A.[Abderrahmane],
Combination of Roadside and In-vehicle Sensors for Extensive Visibility Range Monitoring,
AVSBS09(388-393).
IEEE DOI 0909
BibRef

Pöchmüller, W.[Werner], Kuehnle, G.[Goetz],
Method for determining visibility,
US_Patent7,274,386, Sep 25, 2007
WWW Link. 2 sensors, ration of contrast. BibRef 0709

Treibitz, T.[Tali], Schechner, Y.Y.[Yoav Y.],
Active Polarization Descattering,
PAMI(31), No. 3, March 2009, pp. 385-399.
IEEE DOI 0902
BibRef
Earlier:
Instant 3Descatter,
CVPR06(II: 1861-1868).
IEEE DOI 0606
Nonscanning method that uses active illumination. Range limits due to illumination. Underwater application also. Remove the scattering in fog, etc. BibRef

Hautiere, N., Tarel, J.P., Aubert, D.,
Mitigation of Visibility Loss for Advanced Camera-Based Driver Assistance,
ITS(11), No. 2, June 2010, pp. 474-484.
IEEE DOI 1007
BibRef

Gallen, R.[Romain], Hautière, N.[Nicolas], Dumont, E.[Eric],
Static Estimation of the Meteorological Visibility Distance in Night Fog with Imagery,
IEICE(E93-D), No. 7, July 2010, pp. 1780-1787.
WWW Link. 1008
BibRef
Earlier: MVA09(300-).
PDF File. 0905
BibRef

Tripathi, A.K.[Abhishek Kumar], Mukhopadhyay, S.[Sudipta],
Single image fog removal using anisotropic diffusion,
IET-IPR(6), No. 7, 2012, pp. 966-975.
DOI Link 1211
BibRef

Tripathi, A.K.[Abhishek Kumar], Mukhopadhyay, S.[Sudipta],
Efficient fog removal from video,
SIViP(8), No. 8, November 2014, pp. 1431-1439.
WWW Link. 1411
BibRef

Huang, S.C.[Shih-Chia], Chen, B.H.[Bo-Hao], Cheng, Y.J.[Yi-Jui],
An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems,
ITS(15), No. 5, October 2014, pp. 2321-2332.
IEEE DOI 1410
image colour analysis BibRef

Gallen, R., Cord, A., Hautiere, N., Dumont, E., Aubert, D.,
Nighttime Visibility Analysis and Estimation Method in the Presence of Dense Fog,
ITS(16), No. 1, February 2015, pp. 310-320.
IEEE DOI 1502
Cameras BibRef

Negru, M., Nedevschi, S., Peter, R.I.,
Exponential Contrast Restoration in Fog Conditions for Driving Assistance,
ITS(16), No. 4, August 2015, pp. 2257-2268.
IEEE DOI 1508
Atmospheric modeling BibRef

Choi, L.K.[Lark Kwon], You, J.[Jaehee], Bovik, A.C.,
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging,
IP(24), No. 11, November 2015, pp. 3888-3901.
IEEE DOI 1509
BibRef
Earlier:
Referenceless perceptual image defogging,
Southwest14(165-168)
IEEE DOI 1406
fog. image enhancement BibRef

Yi, L.[Li], Thies, B.[Boris], Zhang, S.[Suping], Shi, X.M.[Xiao-Meng], Bendix, J.[Jörg],
Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection,
RS(8), No. 1, 2016, pp. 8.
DOI Link 1602
BibRef

Li, Y., Hoogeboom, P., Russchenberg, H.W.J.,
A Novel Radar-Based Visibility Estimator,
GeoRS(55), No. 6, June 2017, pp. 3150-3168.
IEEE DOI 1706
Attenuation, Clouds, Estimation, Radar antennas, Radar measurements, Radar remote sensing, Attenuation, drop size distribution (DSD), estimation method, fog, millimeter-wave radar, reflection, visibility, (Vis) BibRef

Chu, W.T.[Wei-Ta], Zheng, X.Y.[Xiang-You], Ding, D.S.[Ding-Shiuan],
Camera as weather sensor: Estimating weather information from single images,
JVCIR(46), No. 1, 2017, pp. 233-249.
Elsevier DOI 1706
Cross-platform, data, association BibRef

Jiang, Y., Sun, C., Zhao, Y., Yang, L.,
Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth,
IP(26), No. 7, July 2017, pp. 3397-3409.
IEEE DOI 1706
Adaptive optics, Atmospheric modeling, Feature extraction, Optical imaging, Optical saturation, Optical scattering, Optical sensors, Fog, defog, fog density, optical depth, surrogate, model BibRef

Jia, Y.H.[Yu-Han], Wu, J.P.[Jian-Ping], Ben-Akiva, M.[Moshe], Seshadri, R.[Ravi], Du, Y.[Yiman],
Rainfall-integrated traffic speed prediction using deep learning method,
IET-ITS(11), No. 9, November 2017, pp. 531-536.
DOI Link 1710
BibRef

Jeong, K.[Kyeongmin], Choi, K.Y.[Kwang-Yeon], Kim, D.[Donghwan], Song, B.C.[Byung Cheol],
Fast Fog Detection for De-Fogging of Road Driving Images,
IEICE(E101-D), No. 2, February 2018, pp. 473-480.
WWW Link. 1802
BibRef

Kim, K.[Kyungil], Kim, S.[Soohyun], Kim, K.S.[Kyung-Soo],
Effective image enhancement techniques for fog-affected indoor and outdoor images,
IET-IPR(12), No. 4, April 2018, pp. 465-471.
DOI Link 1804
BibRef

Liu, J.L.[Jian-Lei],
Visibility distance estimation in foggy situations and single image dehazing based on transmission computation model,
IET-IPR(12), No. 7, July 2018, pp. 1237-1244.
DOI Link 1806
BibRef

Wang, Z.[Zheng], Zheng, R.C.[Ren-Cheng], Kaizuka, T.[Tsutomu], Nakano, K.[Kimihiko],
Influence of haptic guidance on driving behaviour under degraded visual feedback conditions,
IET-ITS(12), No. 6, August 2018, pp. 454-462.
DOI Link 1807
BibRef

Gao, Y.[Yin], Su, Y.J.[Yi-Jing], Li, Q.M.[Qi-Ming], Li, J.[Jun],
Single Fog Image Restoration with Multi-Focus Image Fusion,
JVCIR(55), 2018, pp. 586-595.
Elsevier DOI 1809
Image restoration, Histogram analysis, Adaptive boundary constraint, Multi-focus image fusion BibRef

Pei, L.[Liu], Yuan, X.[Xue], Dai, X.R.[Xue-Rui],
MWNet: object detection network applicable for different weather conditions,
IET-ITS(13), No. 9, September 2019, pp. 1394-1400.
DOI Link 1908
BibRef

Sarkar, M.[Manas], Sarkar, P.R.[Priyanka Rakshit], Mondal, U.[Ujjwal], Nandi, D.[Debashis],
Empirical wavelet transform-based fog removal via dark channel prior,
IET-IPR(14), No. 6, 11 May 2020, pp. 1170-1179.
DOI Link 2005
BibRef

Weston, M.[Michael], Temimi, M.[Marouane],
Application of a Nighttime Fog Detection Method Using SEVIRI Over an Arid Environment,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Sakaridis, C.[Christos], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Semantic Foggy Scene Understanding with Synthetic Data,
IJCV(126), No. 9, September 2018, pp. 973-992.
Springer DOI 1809
BibRef
And:
ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding,
ICCV21(10745-10755)
IEEE DOI 2203
Dataset, Haze. Not just dehazing, actually understand the scene. Training, Image segmentation, Visualization, Rain, Snow, Semantics, Datasets and evaluation, Scene analysis and understanding, Vision for robotics and autonomous vehicles BibRef

Dai, D.X.[Deng-Xin], Sakaridis, C.[Christos], Hecker, S.[Simon], Van Gool, L.J.[Luc J.],
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding,
IJCV(128), No. 5, May 2020, pp. 1182-1204.
Springer DOI 2005
BibRef
Earlier: A2, A1, A3, A4:
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding,
ECCV18(XIII: 707-724).
Springer DOI 1810
BibRef

Han, J.H.[Ji-Hye], Suh, M.S.[Myoung-Seok], Yu, H.Y.[Ha-Yeong], Roh, N.Y.[Na-Young],
Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Dev Roy, S., Kanti Bhowmik, M.,
Annotation and Benchmarking of a Video Dataset under Degraded Complex Atmospheric Conditions and Its Visibility Enhancement Analysis for Moving Object Detection,
CirSysVideo(31), No. 3, March 2021, pp. 844-862.
IEEE DOI 2103
Object detection, Atmospheric modeling, Benchmark testing, Lighting, Rain, Atmospheric/weather conditions, performance evaluation BibRef

Dev Roy, S., Kanti Bhowmik, M., Oakley, J.,
A Ground Truth Annotated Video Dataset for Moving Object Detection in Degraded Atmospheric Outdoor Scenes,
ICIP18(1318-1322)
IEEE DOI 1809
Dataset, Object Detection. Object detection, Lighting, Meteorology, Cameras, Image restoration, Streaming media, Atmospheric measurements, Image Enhancement BibRef

Mehra, A.[Aryan], Mandal, M.[Murari], Narang, P.[Pratik], Chamola, V.[Vinay],
ReViewNet: A Fast and Resource Optimized Network for Enabling Safe Autonomous Driving in Hazy Weather Conditions,
ITS(22), No. 7, July 2021, pp. 4256-4266.
IEEE DOI 2107
BibRef
And: Correction: ITS(23), No. 3, March 2022, pp. 2888-2888.
IEEE DOI 2203
Image color analysis, Atmospheric modeling, Autonomous vehicles, Task analysis, Scattering, Meteorology, Vehicular vision, dehazing, lightweight BibRef

Du, P.[Pei], Zeng, Z.[Zhe], Zhang, J.W.[Jing-Wei], Liu, L.[Lu], Yang, J.C.[Jian-Chang], Qu, C.P.[Chuan-Ping], Jiang, L.[Li], Liu, S.W.[Shan-Wei],
Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, L.[Lin], Zhu, A.[Anqi], Zhao, S.Y.[Shi-Yu], Zhou, Y.C.[Yi-Cong],
Simulation of Atmospheric Visibility Impairment,
IP(30), 2021, pp. 8713-8726.
IEEE DOI 2110
Atmospheric modeling, Computational modeling, Simulation, Games, Annotations, Focusing, Engines, Atmospheric visibility impairment, synthetic datasets BibRef

Guo, X.F.[Xiao-Fei], Wan, J.H.[Jian-Hua], Liu, S.W.[Shan-Wei], Xu, M.M.[Ming-Ming], Sheng, H.[Hui], Yasir, M.[Muhammad],
A scSE-LinkNet Deep Learning Model for Daytime Sea Fog Detection,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Ye, J.[Jin], Liu, L.[Lei], Wu, Y.[Yi], Yang, W.Y.[Wan-Ying], Ren, H.[Hong],
Using Machine Learning Algorithm to Detect Blowing Snow and Fog in Antarctica Based on Ceilometer and Surface Meteorology Systems,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Liao, L.[Liang], Chen, W.[Wenyi], Xiao, J.[Jing], Wang, Z.[Zheng], Lin, C.W.[Chia-Wen], Satoh, S.[Shin'ichi],
Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion,
IP(31), 2022, pp. 3525-3540.
IEEE DOI 2205
Semantics, Adaptation models, Image segmentation, Training, Meteorology, Data models, Atmospheric modeling, Label diffusion, unsupervised domain adaptation BibRef

Guo, Y.[Ying], Liang, R.L.[Rui-Lin], Cui, Y.K.[You-Kai], Zhao, X.M.[Xiang-Mo], Meng, Q.[Qiang],
A domain-adaptive method with cycle perceptual consistency adversarial networks for vehicle target detection in foggy weather,
IET-ITS(16), No. 7, 2022, pp. 971-981.
DOI Link 2206
BibRef

Liu, Z.[Ze], Cai, Y.F.[Ying-Feng], Wang, H.[Hai], Chen, L.[Long], Gao, H.B.[Hong-Bo], Jia, Y.[Yunyi], Li, Y.C.[Yi-Cheng],
Robust Target Recognition and Tracking of Self-Driving Cars With Radar and Camera Information Fusion Under Severe Weather Conditions,
ITS(23), No. 7, July 2022, pp. 6640-6653.
IEEE DOI 2207
Cameras, Radar, Autonomous vehicles, Data integration, Target recognition, Signal processing algorithms, Sensors, self-driving cars BibRef

Blin, R.[Rachel], Ainouz, S.[Samia], Canu, S.[Stéphane], Meriaudeau, F.[Fabrice],
The PolarLITIS Dataset: Road Scenes Under Fog,
ITS(23), No. 8, August 2022, pp. 10753-10762.
IEEE DOI 2208
Roads, Meteorology, Mathematical model, Stokes parameters, Sensors, Cameras, Autonomous vehicles, Road scene analysis, object detection BibRef

Yang, T.[Tao], Li, Y.[You], Ruichek, Y.[Yassine], Yan, Z.[Zhi],
Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach,
ITS(23), No. 8, August 2022, pp. 11227-11236.
IEEE DOI 2208
Laser radar, Meteorology, Measurement by laser beam, Laser modes, Surface emitting lasers, Mathematical model, Predictive models, autonomous driving BibRef

Boudala, F.S.[Faisal S.], Wu, D.[Di], Isaac, G.A.[George A.], Gultepe, I.[Ismail],
Seasonal and Microphysical Characteristics of Fog at a Northern Airport in Alberta, Canada,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Li, Z.[Zhi], Wu, X.[Xing], Wang, J.[Jianjia], Guo, Y.[Yike],
Weather-degraded image semantic segmentation with multi-task knowledge distillation,
IVC(127), 2022, pp. 104554.
Elsevier DOI 2211
Adverse weather, Road scene, Knowledge distillation, Image enhancement, Semantic segmentation BibRef

You, J.[Jing], Jia, S.C.[Shao-Cheng], Pei, X.[Xin], Yao, D.[Danya],
DMRVisNet: Deep Multihead Regression Network for Pixel-Wise Visibility Estimation Under Foggy Weather,
ITS(23), No. 11, November 2022, pp. 22354-22366.
IEEE DOI 2212
Estimation, Meteorology, Feature extraction, Instruments, Convolutional neural networks, Cameras, Safety, supervised learning BibRef

Tang, Y.Z.[Yu-Zhu], Yang, P.[Pinglv], Zhou, Z.[Zeming], Zhao, X.F.[Xiao-Feng],
Daytime Sea Fog Detection Based on a Two-Stage Neural Network,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Li, J.L.[Jin-Long], Xu, R.S.[Run-Sheng], Ma, J.[Jin], Zou, Q.[Qin], Ma, J.Q.[Jia-Qi], Yu, H.K.[Hong-Kai],
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather,
WACV23(612-622)
IEEE DOI 2302
Measurement, Training, Adaptation models, Transfer learning, Object detection, Feature extraction, visual reasoning BibRef

Zhang, Y.X.[Yu-Xiao], Carballo, A.[Alexander], Yang, H.[Hanting], Takeda, K.[Kazuya],
Perception and sensing for autonomous vehicles under adverse weather conditions: A survey,
PandRS(196), 2023, pp. 146-177.
Elsevier DOI 2302
Perception and sensing, Adverse weather conditions, Autonomous driving, LiDAR, Sensor fusion, Deep learning BibRef

Liu, F.X.[Fu-Xiang], Zang, C.[Chen], Li, L.[Lei], Xu, C.F.[Chun-Feng], Luo, J.M.[Jing-Min],
ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network,
IEICE(E106-D), No. 4, April 2023, pp. 488-494.
WWW Link. 2304
BibRef

Huang, S.C.[Shih-Chia], Jaw, D.W.[Da-Wei], Hoang, Q.V.[Quoc-Viet], Le, T.H.[Trung-Hieu],
3FL-Net: An Efficient Approach for Improving Performance of Lightweight Detectors in Rainy Weather Conditions,
ITS(24), No. 4, April 2023, pp. 4293-4305.
IEEE DOI 2304
Feature extraction, Rain, Detectors, Object detection, Meteorology, Training, Task analysis, Object detection, lightweight detector, CNN BibRef

Boudala, F.S.[Faisal S.], Milbrandt, J.A.[Jason A.],
Solid Precipitation and Visibility Measurements at the Centre for Atmospheric Research Experiments in Southern Ontario and Bratt's Lake in Southern Saskatchewan,
RS(15), No. 16, 2023, pp. 4079.
DOI Link 2309
BibRef

Zhai, B.[Ben], Wang, Y.L.[Yan-Li], Wu, B.[Bing],
An ensemble learning method for low visibility prediction on freeway using meteorological data,
IET-ITS(17), No. 11, 2023, pp. 2237-2250.
DOI Link 2311
intelligent transportation systems, prediction theory, road safety, sustainable development BibRef

Han, J.H.[Ji-Hye], Suh, M.S.[Myoung-Seok], Yu, H.Y.[Ha-Yeong], Kim, S.H.[So-Hyeong],
Improvement of High-Resolution Daytime Fog Detection Algorithm Using GEO-KOMPSAT-2A/Advanced Meteorological Imager Data with Optimization of Background Field and Threshold Values,
RS(16), No. 11, 2024, pp. 2031.
DOI Link 2406
BibRef

Zhang, C.[Cheng], Wang, H.[Hai], Cai, Y.F.[Ying-Feng], Chen, L.[Long], Li, Y.C.[Yi-Cheng],
TransFusion: Multi-Modal Robust Fusion for 3D Object Detection in Foggy Weather Based on Spatial Vision Transformer,
ITS(25), No. 9, September 2024, pp. 10652-10666.
IEEE DOI Code:
WWW Link. 2409
Laser radar, Point cloud compression, Meteorology, Radar, Cameras, Radar imaging, 3D object detection, LiDAR BibRef

Huang, Q.[Qin], Zeng, P.[Peng], Guo, X.W.[Xiao-Wei], Lyu, J.J.[Jing-Jing],
Utilizing Machine Learning and Multi-Station Observations to Investigate the Visibility of Sea Fog in the Beibu Gulf,
RS(16), No. 18, 2024, pp. 3392.
DOI Link 2410
BibRef

Xie, Y.M.[Yi-Ming], Wei, H.[Henglu], Liu, Z.[Zhenyi], Wang, X.Y.[Xiao-Yu], Ji, X.Y.[Xiang-Yang],
SynFog: A Photorealistic Synthetic Fog Dataset Based on End-to-End Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving,
CVPR24(21763-21772)
IEEE DOI 2410
Atmospheric modeling, Pipelines, Lighting, Light scattering, Rendering (computer graphics), Optics, Real-time systems BibRef

Jang, D.G.[Dong-Gon], Lee, S.[Sunhyeok], Choi, G.[Gyuwon], Lee, Y.[Yejin], Son, S.[Sanghyeok], Kim, D.S.[Dae-Shik],
Energy-Based Domain Adaptation Without Intermediate Domain Dataset for Foggy Scene Segmentation,
IP(33), 2024, pp. 6143-6157.
IEEE DOI Code:
WWW Link. 2411
Training, Meteorology, Adaptation models, Radio frequency, Semantic segmentation, Reliability, Semantics, Predictive models, features statistics matching BibRef

Ding, Y.N.[Yi-Ning], Wallace, A.M.[Andrew M.], Wang, S.[Sen],
Estimating Fog Parameters from an Image Sequence using Non-linear Optimisation,
WACV24(i-ix)
IEEE DOI 2404
Visualization, Parameter estimation, Systematics, Estimation, Scattering, Data collection, Image sequences, Algorithms, Autonomous Driving BibRef

Shin, U.[Ukcheol], Park, J.[Jinsun], Kweon, I.S.[In So],
Deep Depth Estimation from Thermal Image,
CVPR23(1043-1053)
IEEE DOI 2309

WWW Link. for bad weather. BibRef

Yang, X.[Xin], Mi, M.B.[Michael Bi], Yuan, Y.[Yuan], Wang, X.[Xin], Tan, R.T.[Robby T.],
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation,
ACCV22(VI:303-318).
Springer DOI 2307
BibRef

Yao, C.T.[Cheng-Tang], Yu, L.D.[Li-Dong],
FoggyStereo: Stereo Matching with Fog Volume Representation,
CVPR22(13033-13042)
IEEE DOI 2210
Costs, Fuses, Stacking, Scattering, Estimation, 3D from multi-view and sensors BibRef

Mirza, M.J.[M. Jehanzeb], Masana, M.[Marc], Possegger, H.[Horst], Bischof, H.[Horst],
An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions,
V4AS22(3000-3010)
IEEE DOI 2210
Training, Adaptation models, Snow, Scalability, Object detection, Task analysis, Autonomous vehicles BibRef

Lee, S.[Sohyun], Son, T.[Taeyoung], Kwak, S.[Suha],
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation,
CVPR22(18889-18899)
IEEE DOI 2210
Training, Image segmentation, Visualization, Image analysis, Shape, Filtering, Semantics, Segmentation, grouping and shape analysis, Scene analysis and understanding BibRef

Ma, X.Z.[Xian-Zheng], Wang, Z.X.[Zhi-Xiang], Zhan, Y.C.[Ya-Cheng], Zheng, Y.Q.[Yin-Qiang], Wang, Z.[Zheng], Dai, D.X.[Deng-Xin], Lin, C.W.[Chia-Wen],
Both Style and Fog Matter: Cumulative Domain Adaptation for Semantic Foggy Scene Understanding,
CVPR22(18900-18909)
IEEE DOI 2210
Bridges, Uncertainty, Codes, Semantics, Pipelines, Navigation and autonomous driving BibRef

Diaz-Ruiz, C.A.[Carlos A.], Xia, Y.Y.[You-Ya], You, Y.R.[Yu-Rong], Nino, J.[Jose], Chen, J.[Junan], Monica, J.[Josephine], Chen, X.Y.[Xiang-Yu], Luo, K.[Katie], Wang, Y.[Yan], Emond, M.[Marc], Chao, W.L.[Wei-Lun], Hariharan, B.[Bharath], Weinberger, K.Q.[Kilian Q.], Campbell, M.[Mark],
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions,
CVPR22(21351-21360)
IEEE DOI 2210
Point cloud compression, Rain, Snow, Roads, Pose estimation, Sensor systems and applications, Datasets and evaluation, Vision applications and systems BibRef

Blin, R.[Rachel], Ainouz, S.[Samia], Canu, S.[Stéphane], Meriaudeau, F.[Fabrice],
Multimodal Polarimetric and Color Fusion for Road Scene Analysis In Adverse Weather Conditions,
ICIP21(3338-3342)
IEEE DOI 2201
Image analysis, Image color analysis, Roads, Imaging, Color, Feature extraction, Autonomous vehicles, multimodal fusion, deep learning. BibRef

Musat, V.[Valentina], Fursa, I.[Ivan], Newman, P.[Paul], Cuzzolin, F.[Fabio], Bradley, A.[Andrew],
Multi-weather city: Adverse weather stacking for autonomous driving,
AVVision21(2906-2915)
IEEE DOI 2112
Training, Visualization, Stacking, Pipelines, Computer architecture, Robustness, Sensors BibRef

Qian, K.[Kun], Zhu, S.L.[Shi-Lin], Zhang, X.Y.[Xin-Yu], Li, L.E.[Li Erran],
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals,
CVPR21(444-453)
IEEE DOI 2111
Training, Meteorological radar, Visualization, Laser radar, Vehicle detection, Sensor phenomena and characterization, Cameras BibRef

Fourt, O.[Olivier], Tarel, J.P.[Jean-Philippe],
Visibility Restoration in Infra-Red Images,
ICPR21(6935-6940)
IEEE DOI 2105
Visualization, Rain, Snow, Roads, Noise reduction, Scattering, Cameras BibRef

Yan, W.D.[Wen-Ding], Tan, R.T.[Robby T.], Dai, D.X.[Deng-Xin],
Nighttime Defogging Using High-low Frequency Decomposition and Grayscale-color Networks,
ECCV20(XII: 473-488).
Springer DOI 2010
BibRef

Guan, J., Madani, S., Jog, S., Gupta, S., Hassanieh, H.,
Through Fog High-Resolution Imaging Using Millimeter Wave Radar,
CVPR20(11461-11470)
IEEE DOI 2008
Radar imaging, Millimeter wave radar, Automobiles, Image resolution BibRef

Yan, W., Sharma, A., Tan, R.T.,
Optical Flow in Dense Foggy Scenes Using Semi-Supervised Learning,
CVPR20(13256-13265)
IEEE DOI 2008
Optical imaging, Adaptive optics, Training, Integrated optics, Optical variables control, Optical fiber networks, Estimation BibRef

Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., Heide, F.,
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather,
CVPR20(11679-11689)
IEEE DOI 2008
Sensors, Laser radar, Cameras, Snow, Object detection BibRef

Xu, X., Zhu, X., Mu, N., Chen, L., Tian, J.,
Saliency Computational Model for Foggy Images by Fusing Frequency and Spatial Cues,
ICIP19(959-963)
IEEE DOI 1910
Saliency Computation, Frequency Domain, Spatial Domain, Discrete Stationary Wavelet Transform, Foggy Image BibRef

Galdran, A., Costa, P., Vazquez-Corral, J., Campilho, A.,
Weakly Supervised Fog Detection,
ICIP18(2875-2879)
IEEE DOI 1809
Training, Task analysis, Training data, Standards, Data models, Predictive models, Channel estimation, Fog Detection, Multiple-Instance Learning BibRef

Li, K., Li, Y., You, S., Barnes, N.,
Photo-Realistic Simulation of Road Scene for Data-Driven Methods in Bad Weather,
PBVDL17(491-500)
IEEE DOI 1802
Autonomous vehicles, Computational modeling, Image color analysis, Meteorology, Rendering (computer graphics), Roads BibRef

González-Jorge, H., Díaz-Vilariño, L., Lorenzo, H., Arias, P.,
Evaluation Of Driver Visibility From Mobile Lidar Data And Weather Conditions,
ISPRS16(B1: 577-582).
DOI Link 1610
BibRef

Almazan, E.J.[Emilio J.], Qian, Y.M.[Yi-Ming], Elder, J.H.[James H.],
Road Segmentation for Classification of Road Weather Conditions,
CVRoads16(I: 96-108).
Springer DOI 1611
BibRef
And: A2, A1, A3:
Evaluating features and classifiers for road weather condition analysis,
ICIP16(4403-4407)
IEEE DOI 1610
Cameras BibRef

Alami, S., Ezzine, A., Elhassouni, F.,
Local Fog Detection Based on Saturation and RGB-Correlation,
CGiV16(1-5)
IEEE DOI 1608
correlation methods BibRef

Li, C.[Congli], Lu, W.J.[Wen-Jun], Xue, S.[Song], Shi, Y.C.[Yong-Chang], Sun, X.N.[Xiao-Ning],
Quality assessment of polarization analysis images in foggy conditions,
ICIP14(551-555)
IEEE DOI 1502
Feature extraction BibRef

Kawarabuki, H.[Hiroshi], Onoguchi, K.[Kazunori],
Snowfall Detection in a Foggy Scene,
ICPR14(877-882)
IEEE DOI 1412
Cameras BibRef

Gibson, K.B.[Kristofor B.], Nguyen, T.Q.[Truong Q.],
Fast single image fog removal using the adaptive Wiener filter,
ICIP13(714-718)
IEEE DOI 1402
Atmospheric modeling BibRef

Veeramani, T.[Thangamani], Rajagopalan, A.N.[Ambasamudram N.], Seetharaman, G.[Guna],
Restoration of foggy and motion-blurred road scenes,
ICIP13(928-932)
IEEE DOI 1402
Cameras BibRef

Caraffa, L.[Laurent], Tarel, J.P.[Jean-Philippe],
Stereo Reconstruction and Contrast Restoration in Daytime Fog,
ACCV12(IV:13-25).
Springer DOI 1304
BibRef

Halmaoui, H.[Houssam], Cord, A.[Aurelien], Hautiere, N.[Nicolas],
Contrast restoration of road images taken in foggy weather,
CVVT11(2057-2063).
IEEE DOI 1201
BibRef

Yuan, H.Z.[Hong-Zhao], Zhang, X.Q.[Xiu-Qiong], Feng, Z.L.[Zi-Liang],
Horizon detection in foggy aerial image,
IASP10(191-194).
IEEE DOI 1004
BibRef

Kitayama, T.[Takashi], Sakai, M.[Masatoshi], Kato, T.[Takashi], Yamada, M.[Muneo], Nakano, T.[Tomoaki], Yamamoto, S.[Shin], Yamamoto, O.[Osami], Yamanishi, M.[Masahiko], Matsumoto, H.[Hiroshi],
On a Technique for Evaluating Performance of Wipers Based on Forward Visibility,
MVA09(265-).
PDF File. 0905
BibRef

Hiramatsu, T.[Tomoki], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
A Kalman filter-based approach for adaptive restoration of in-vehicle camera foggy images,
ICIP08(3160-3163).
IEEE DOI 0810
BibRef

Ngo, H.T.[Hau T.], Tao, L.[Li], Zhang, M.[Ming], Livingston, A., Asari, V.K.,
A Visibility Improvement System for Low Vision Drivers by Nonlinear Enhancement of Fused Visible and Infrared Video,
VisImpaired05(III: 25-25).
IEEE DOI 0507
BibRef

Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Driver Modeling, Behavior Models, Analysis .