Gupta, H.[Honey],
Mitra, K.[Kaushik],
Toward Unaligned Guided Thermal Super-Resolution,
IP(31), 2022, pp. 433-445.
IEEE DOI
2112
Superresolution, Cameras, Image resolution, Feature extraction,
Task analysis, Imaging, Thermal sensors, Unaligned, thermal,
deep neural network
BibRef
Zhu, L.Z.[Lin-Zhen],
Wu, R.J.[Ren-Jie],
Lee, B.G.[Boon-Giin],
Nkenyereye, L.[Lionel],
Chung, W.Y.[Wan-Young],
Xu, G.[Gen],
FEGAN: A Feature-Oriented Enhanced GAN for Enhancing Thermal Image
Super-Resolution,
SPLetters(31), 2024, pp. 541-545.
IEEE DOI
2402
Training, Feature extraction, Iron, Image reconstruction,
Image edge detection, Imaging, Measurement, thermal imaging
BibRef
Chen, X.H.[Xiao-Hui],
Chen, L.[Lin],
Chen, L.J.[Ling-Jun],
Chen, P.[Peng],
Sheng, G.Q.[Guan-Qun],
Yu, X.S.[Xiao-Sheng],
Zou, Y.B.[Yao-Bin],
Modeling Thermal Infrared Image Degradation and Real-World
Super-Resolution Under Background Thermal Noise and Streak
Interference,
CirSysVideo(34), No. 7, July 2024, pp. 6194-6206.
IEEE DOI
2407
Degradation, Thermal noise, Interference, Feature extraction,
Superresolution, Image reconstruction, Thermal degradation, streak interference
BibRef
Huang, J.X.[Jia-Xin],
Wang, H.[Huicong],
Li, Y.H.[Yu-Han],
Liu, S.J.[Shi-Jian],
A Feature-Driven Inception Dilated Network for Infrared Image
Super-Resolution Reconstruction,
RS(16), No. 21, 2024, pp. 4033.
DOI Link
2411
BibRef
Quero, C.O.[Carlos Osorio],
Durini, D.[Daniel],
Rangel-Magdaleno, J.[Jose],
Martinez-Carranza, J.[Jose],
Ramos-Garcia, R.[Ruben],
Deep-learning blurring correction of images obtained from NIR
single-pixel imaging,
JOSA-A(40), No. 8, August 2023, pp. 1491-1499.
DOI Link
2503
Lidar, Machine vision, Radar, Single pixel imaging,
Spatial light modulators, Spatial resolution
BibRef
Huang, Y.S.[Yong-Song],
Miyazaki, T.[Tomo],
Liu, X.F.[Xiao-Feng],
Dong, Y.F.[Ya-Fei],
Omachi, S.[Shinichiro],
Texture and noise dual adaptation for infrared image super-resolution,
PR(163), 2025, pp. 111449.
Elsevier DOI Code:
WWW Link.
2503
Super-resolution, Infrared imaging, Domain adaptation, Deep learning
BibRef
Shyam, P.[Pranjay],
Yoo, H.[HyunJin],
Lightweight Thermal Super-Resolution and Object Detection for Robust
Perception in Adverse Weather Conditions,
WACV24(7456-7467)
IEEE DOI
2404
Weather. Image sensors, Superresolution, Lighting, Object detection,
Detectors, Thermal sensors, Autonomous Driving
BibRef
Puttagunta, R.S.[Raghunath Sai],
Kathariya, B.[Birendra],
Li, Z.[Zhu],
York, G.[George],
Multi-Scale Feature Fusion using Channel Transformers for Guided
Thermal Image Super Resolution,
PBVS24(3086-3095)
IEEE DOI
2410
Visualization, Limiting, Superresolution, Imaging, Transformer cores,
Transformers, Guided Super Resolution, Channel Transformer
BibRef
Jiang, H.C.[Hong-Cheng],
Chen, Z.[ZhiQiang],
Flexible Window-based Self-attention Transformer in Thermal Image
Super-Resolution,
PBVS24(3076-3085)
IEEE DOI Code:
WWW Link.
2410
Attention mechanisms, Superresolution, Semantics, Noise, Landsat,
Transformers, Thermal noise
BibRef
Cortés-Mendez, C.[Carlos],
Hayet, J.B.[Jean-Bernard],
Exploring the usage of diffusion models for thermal image
super-resolution: a generic, uncertainty-aware approach for guided
and non-guided schemes,
PBVS24(3123-3130)
IEEE DOI
2410
Uncertainty, Frequency-domain analysis, Superresolution, Refining,
Imaging, Diffusion models, Diffusion models, Super-resolution, Thermal images
BibRef
Kasliwal, A.[Aditya],
Seth, P.[Pratinav],
Rallabandi, S.[Sriya],
Singhal, S.[Sanchit],
CoReFusion: Contrastive Regularized Fusion for Guided Thermal
Super-Resolution,
PBVS23(507-514)
IEEE DOI
2309
BibRef
Wang, K.[Kai],
Sun, Q.G.[Qi-Gong],
Wang, Y.C.[Yi-Cheng],
Wei, H.Y.[Hui-Yuan],
Lv, C.H.[Chong-Hua],
Tian, X.L.[Xiao-Lin],
Liu, X.[Xu],
CIPPSRNet: A Camera Internal Parameters Perception Network Based
Contrastive Learning for Thermal Image Super-Resolution,
PBVS22(341-348)
IEEE DOI
2210
Training, Degradation, Visualization, Superresolution, Cameras,
Robustness
BibRef
Gutierrez, N.B.[Nolan B.],
Beksi, W.J.[William J.],
Thermal Image Super-Resolution Using Second-Order Channel Attention
with Varying Receptive Fields,
CVS21(3-13).
Springer DOI
2109
BibRef
Prajapati, K.[Kalpesh],
Chudasama, V.[Vishal],
Patel, H.[Heena],
Sarvaiya, A.[Anjali],
Upla, K.[Kishor],
Raja, K.[Kiran],
Ramachandra, R.[Raghavendra],
Busch, C.[Christoph],
Channel Split Convolutional Neural Network (ChaSNet) for Thermal
Image Super-Resolution,
PBVS21(4363-4372)
IEEE DOI
2109
BibRef
Earlier: A2, A3, A1, A5, A7, A6, A8, Only:
TherISuRNet:
A Computationally Efficient Thermal Image Super-Resolution Network,
PBVS20(388-397)
IEEE DOI
2008
Image sensors, Visualization, Thermal factors, Superresolution,
Thermal sensors, Optical imaging, Thermal noise.
Cameras, Spatial resolution, Feature extraction, Training, Computer architecture
BibRef
Prajapati, K.[Kalpesh],
Chudasama, V.[Vishal],
Upla, K.[Kishor],
Raia, K.[Kiran],
Ramachandra, R.[Raghavendra],
Busch, C.[Christoph],
Channel Split Convolutional Neural Network for Single Image
Super-Resolution (CSISR),
FG21(1-8)
IEEE DOI
2303
Performance evaluation, Training, Image synthesis,
Computational modeling, Superresolution, Memory management,
Computational efficiency
BibRef
Nathan, S.[Sabari],
Kansal, P.[Priya],
Leveraging Multi scale Backbone with Multilevel supervision for
Thermal Image Super Resolution,
PBVS21(4327-4333)
IEEE DOI
2109
BibRef
Earlier: A2, A1:
A Multi-Level Supervision Model:
A novel approach for Thermal Image Super Resolution,
PBVS20(426-431)
IEEE DOI
2008
Training, Convolution, Superresolution,
Robustness.
Spatial resolution, Training, Task analysis,
Image edge detection, Convolution
BibRef
Rivadeneira, R.E.[Rafael E.],
Suárez, P.L.[Patricia L.],
Sappa, A.D.[Angel D.],
Vintimilla, B.X.[Boris X.],
Thermal Image Super-Resolution Through
Deep Convolutional Neural Network,
ICIAR19(II:417-426).
Springer DOI
1909
BibRef
Chen, X.,
Zhai, G.,
Wang, J.,
Hu, C.,
Chen, Y.,
Color guided thermal image super resolution,
VCIP16(1-4)
IEEE DOI
1701
Cameras
BibRef
Tang, H.X.[Hui-Xuan],
Zhang, X.P.[Xiao-Peng],
Zhuo, S.J.[Shao-Jie],
Chen, F.[Feng],
Kutulakos, K.N.,
Shen, L.[Liang],
High Resolution Photography with an RGB-Infrared Camera,
ICCP15(1-10)
IEEE DOI
1511
colour photography
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Learning for Super Resolution .