Laamanen, H.[Hannu],
Jetsu, T.[Tuija],
Jaaskelainen, T.[Timo],
Parkkinen, J.[Jussi],
Weighted compression of spectral color information,
JOSA-A(25), No. 6, June 2008, pp. 1383-1388.
DOI Link
0711
BibRef
Li, H.Y.[Hong-Yu],
Wu, Z.J.[Zhu-Jing],
Zhang, L.[Lin],
Parkkinen, J.[Jussi],
SR-LLA: A novel spectral reconstruction method based on locally
linear approximation,
ICIP13(2029-2033)
IEEE DOI
1402
Munsell dataset, Spectral reconstruction, locally linear approximation
BibRef
Murakami, Y.[Yuri],
Yamaguchi, M.[Masahiro],
Ohyama, N.[Nagaaki],
Class-based spectral reconstruction based on unmixing of low-resolution
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1107
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Li, Y.Q.[Yu-Qi],
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Locally Linear Embedded Sparse Coding for Spectral Reconstruction
From RGB Images,
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IEEE DOI
1802
Cameras, Dictionaries, Feature extraction, Image color analysis,
Image reconstruction, Image resolution, Training,
spectral reconstruction
BibRef
Nguyen, R.M.H.[Rang M. H.],
Brown, M.S.[Michael S.],
RAW Image Reconstruction Using a Self-contained sRGB-JPEG Image with
Small Memory Overhead,
IJCV(126), No. 6, June 2018, pp. 637-650.
Springer DOI
1804
BibRef
Earlier:
RAW Image Reconstruction Using a Self-Contained sRGB-JPEG Image with
Only 64 KB Overhead,
CVPR16(1655-1663)
IEEE DOI
1612
BibRef
Nguyen, R.M.H.[Rang M. H.],
Prasad, D.K.[Dilip K.],
Brown, M.S.[Michael S.],
Training-Based Spectral Reconstruction from a Single RGB Image,
ECCV14(VII: 186-201).
Springer DOI
1408
BibRef
Earlier: A2, A1, A3:
Quick Approximation of Camera's Spectral Response from Casual
Lighting,
CVPV13(844-851)
IEEE DOI
1403
approximation theory
BibRef
Li, Y.Q.[Yu-Qi],
Wang, C.[Chong],
Zhao, J.Y.[Jie-Yu],
Yuan, Q.S.[Qing-Shu],
Efficient spectral reconstruction using a trichromatic camera via
sample optimization,
VC(34), No. 12, December 2018, pp. 1773-1783.
Springer DOI
1811
BibRef
Han, X.,
Yu, J.,
Luo, J.,
Sun, W.,
Reconstruction From Multispectral to Hyperspectral Image Using
Spectral Library-Based Dictionary Learning,
GeoRS(57), No. 3, March 2019, pp. 1325-1335.
IEEE DOI
1903
geophysical image processing, hyperspectral imaging,
image classification, image fusion, image matching,
spectral library
BibRef
Rout, L.,
ALERT: Adversarial Learning With Expert Regularization Using Tikhonov
Operator for Missing Band Reconstruction,
GeoRS(58), No. 6, June 2020, pp. 4395-4405.
IEEE DOI
2005
Adversarial learning, expert regularization,
missing band reconstruction, remote sensing, Tikhonov operator
BibRef
Wang, B.L.[Ben-Lin],
An, R.[Ru],
Jiang, T.[Tong],
Xing, F.[Fei],
Ju, F.[Feng],
Image Spectral Resolution Enhancement for Mapping Native Plant
Species in a Typical Area of the Three-River Headwaters Region, China,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Li, J.J.[Jiao-Jiao],
Wu, C.X.[Chao-Xiong],
Song, R.[Rui],
Li, Y.S.[Yun-Song],
Xie, W.Y.[Wei-Ying],
Residual Augmented Attentional U-Shaped Network for Spectral
Reconstruction from RGB Images,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Dai, S.F.[Shao-Fei],
Liu, W.[Wenbo],
Wang, Z.Y.[Zheng-Yi],
Li, K.Y.[Kai-Yu],
A Task-Driven Invertible Projection Matrix Learning Algorithm for
Hyperspectral Compressed Sensing,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Paul, S.,
Nagesh Kumar, D.,
Transformation of Multispectral Data to Quasi-Hyperspectral Data
Using Convolutional Neural Network Regression,
GeoRS(59), No. 4, April 2021, pp. 3352-3368.
IEEE DOI
2104
Earth, Data models, Remote sensing, Artificial satellites,
Spatial resolution, Agriculture,
quasi-HS data
BibRef
Park, J.E.[Jeong-Eun],
Kim, G.[Goo],
Hong, S.[Sungwook],
Green Band Generation for Advanced Baseline Imager Sensor Using
Pix2Pix With Advanced Baseline Imager and Advanced Himawari Imager
Observations,
GeoRS(59), No. 8, August 2021, pp. 6415-6423.
IEEE DOI
2108
Role in monitoring water and vegetation information. No green band in GOES-16.
Green products, Satellites, Air pollution, Data models, Training,
Vegetation mapping, Indexes, Artificial intelligence (AI),
satellite remote sensing
BibRef
He, W.[Wei],
Yokoya, N.[Naoto],
Yuan, X.[Xin],
Fast Hyperspectral Image Recovery of Dual-Camera Compressive
Hyperspectral Imaging via Non-Iterative Subspace-Based Fusion,
IP(30), 2021, pp. 7170-7183.
IEEE DOI
2108
Image reconstruction, Hyperspectral imaging, Sensors, Image coding,
Particle measurements, Atmospheric measurements,
fusion
BibRef
Cao, M.[Meng],
Bao, W.X.[Wen-Xing],
Qu, K.[Kewen],
Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank
Tensor Decomposition,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Jameel, S.K.[Samer Kais],
Majidpour, J.[Jafar],
Generating Spectrum Images from Different Types: Visible, Thermal, and
Infrared Based on Autoencoder Architecture (GVTI-AE),
IJIG(22), No. 1 2022, pp. 2250005.
DOI Link
2202
BibRef
Rodríguez-Suárez, B.[Brais],
Quesada-Barriuso, P.[Pablo],
Argüello, F.[Francisco],
Design of CGAN Models for Multispectral Reconstruction in Remote
Sensing,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Wang, L.X.[Li-Xia],
Sole, A.[Aditya],
Hardeberg, J.Y.[Jon Yngve],
Densely Residual Network with Dual Attention for Hyperspectral
Reconstruction from RGB Images,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Stepcenkov, S.[Sergej],
Wilhelm, T.[Thorsten],
Wöhler, C.[Christian],
Learning the Link between Albedo and Reflectance: Machine
Learning-Based Prediction of Hyperspectral Bands from CTX Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Yang, K.X.[Kai-Xiang],
Luo, Y.M.[You-Ming],
Li, M.Y.[Meng-Yao],
Zhong, S.Y.[Shou-Yi],
Liu, Q.[Qiang],
Li, X.H.[Xiu-Hong],
Reconstruction of Sentinel-2 Image Time Series Using Google Earth
Engine,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Xie, S.C.[Shi-Cheng],
Wang, S.[Shun],
Song, C.M.[Chuan-Ming],
Wang, X.H.[Xiang-Hai],
Hyperspectral Image Reconstruction Based on Spatial-Spectral Domains
Low-Rank Sparse Representation,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Peng, M.Y.[Ming-Yuan],
Li, G.Y.[Guo-Yuan],
Zhou, X.Q.[Xiao-Qing],
Ma, C.[Chen],
Zhang, L.[Lifu],
Zhang, X.[Xia],
Shang, K.[Kun],
A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D
Satellite Hyperspectral Data Using SRE-ResNet,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
Hyperspectral swath is narrower than multi-spectral.
BibRef
Huang, F.[Feng],
Chen, Y.T.[Ya-Ting],
Wang, X.S.[Xue-Song],
Wang, S.[Shu],
Wu, X.Y.[Xian-Yu],
Spectral Clustering Super-Resolution Imaging Based on Multispectral
Camera Array,
IP(32), 2023, pp. 1257-1271.
IEEE DOI
2302
Imaging, Apertures, Cameras, Superresolution, Multispectral imaging,
Image reconstruction, Band-pass filters, adaptive kernel
BibRef
Mohamed, A.[Ali],
Emam, A.[Ashraf],
Zoheir, B.[Basem],
SAM-HIT: A Simulated Annealing Multispectral to Hyperspectral Imagery
Data Transformation,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Xiao, S.M.[Shu-Ming],
Zhang, Y.[Ye],
Chang, X.[Xuling],
Xu, J.J.[Jia-Jia],
Compressive sensing reconstruction of hyperspectral images based on
codec space-spectrum joint dense residual network,
IET-IPR(17), No. 3, 2023, pp. 916-931.
DOI Link
2303
BibRef
Mohan, D.[Divya],
Aravinth, J.,
Rajendran, S.[Sankaran],
Reconstruction of Compressed Hyperspectral Image Using SqueezeNet
Coupled Dense Attentional Net,
RS(15), No. 11, 2023, pp. 2734.
DOI Link
2306
BibRef
Ran, R.[Ran],
Deng, L.J.[Liang-Jian],
Jiang, T.X.[Tai-Xiang],
Hu, J.F.[Jin-Fan],
Chanussot, J.[Jocelyn],
Vivone, G.[Gemine],
GuidedNet: A General CNN Fusion Framework via High-Resolution
Guidance for Hyperspectral Image Super-Resolution,
Cyber(53), No. 7, July 2023, pp. 4148-4161.
IEEE DOI
2307
Image reconstruction, Task analysis, Superresolution,
Pansharpening, Hyperspectral imaging, Spatial resolution, Training,
single-image super-resolution (SISR)
BibRef
Palsson, B.[Burkni],
Ulfarsson, M.O.[Magnus O.],
Sveinsson, J.R.[Johannes R.],
Synthesis of Synthetic Hyperspectral Images with Controllable
Spectral Variability Using a Generative Adversarial Network,
RS(15), No. 16, 2023, pp. 3919.
DOI Link
2309
BibRef
Qu, Q.Y.[Qiao-Ying],
Pan, B.[Bin],
Xu, X.[Xia],
Li, T.[Tao],
Shi, Z.W.[Zhen-Wei],
Unmixing Guided Unsupervised Network for RGB Spectral
Super-Resolution,
IP(32), 2023, pp. 4856-4867.
IEEE DOI
2310
BibRef
Zhao, E.[Enyu],
Qu, N.[Nianxin],
Wang, Y.[Yulei],
Gao, C.X.[Cai-Xia],
Spectral Reconstruction from Thermal Infrared Multispectral Image
Using Convolutional Neural Network and Transformer Joint Network,
RS(16), No. 7, 2024, pp. 1284.
DOI Link
2404
BibRef
Chen, H.[Huan],
Zhao, W.C.[Wang-Cai],
Xu, T.F.[Ting-Fa],
Shi, G.[Guokai],
Zhou, S.[Shiyun],
Liu, P.[Peifu],
Li, J.A.[Jian-An],
Spectral-Wise Implicit Neural Representation for Hyperspectral Image
Reconstruction,
CirSysVideo(34), No. 5, May 2024, pp. 3714-3727.
IEEE DOI
2405
Image reconstruction, Signal to noise ratio, Interference, Codes,
Apertures, Modulation, Hyperspectral image reconstruction,
spectral continuity
BibRef
Chen, Y.[Yunlai],
Zhang, X.Y.[Xiao-Yan],
DDSR: Degradation-Aware Diffusion Model for Spectral Reconstruction
from RGB Images,
RS(16), No. 15, 2024, pp. 2692.
DOI Link
2408
BibRef
Zhou, H.Z.[Hao-Zhe],
Liu, Z.[Zhanhao],
Huang, Z.[Zhenpu],
Wang, X.G.[Xu-Guang],
Su, W.[Wen],
Zhang, Y.C.[Yan-Chao],
ICTH: Local-to-Global Spectral Reconstruction Network for
Heterosource Hyperspectral Images,
RS(16), No. 18, 2024, pp. 3377.
DOI Link
2410
BibRef
Cai, Z.[Zeyu],
Hong, R.[Ru],
Lin, X.[Xun],
Yang, J.M.[Ji-Ming],
Ni, Y.L.[You-Liang],
Liu, Z.[Zhen],
Jin, C.Q.[Cheng-Qian],
Da, F.P.[Fei-Peng],
A MLP architecture fusing RGB and CASSI for computational spectral
imaging,
CVIU(249), 2024, pp. 104214.
Elsevier DOI
2412
CASSI: Coded Aperture Snapshot Spectral Imaging.
MLP, Knowledge-transfer, Compressive sensing, CASSI, Snapshot
BibRef
Cao, C.P.[Chi-Peng],
Li, J.[Jie],
Wang, P.[Pan],
Jin, W.Q.[Wei-Qiang],
Zou, R.[Runrun],
Qi, C.[Chun],
Hyperspectral Reconstruction Method Based on Global Gradient
Information and Local Low-Rank Priors,
RS(16), No. 24, 2024, pp. 4759.
DOI Link
2501
BibRef
Li, Y.[Yue],
Wang, X.R.[Xiao-Rui],
Zhang, C.[Chao],
Zhang, Z.G.[Zhong-Gen],
Ren, F.[Fafa],
High-Fidelity Infrared Remote Sensing Image Generation Method Coupled
with the Global Radiation Scattering Mechanism and Pix2PixGAN,
RS(16), No. 23, 2024, pp. 4350.
DOI Link
2501
BibRef
Xu, R.K.[Rui-Kang],
Yao, M.D.[Ming-De],
Chen, C.[Chang],
Wang, L.Z.[Li-Zhi],
Xiong, Z.W.[Zhi-Wei],
Continuous Spatial-Spectral Reconstruction via Implicit Neural
Representation,
IJCV(133), No. 1, January 2025, pp. 106-128.
Springer DOI
2501
BibRef
Earlier:
Continuous Spectral Reconstruction from RGB Images via Implicit Neural
Representation,
MIPI22(78-94).
Springer DOI
2304
BibRef
Wang, N.[Nan],
Mei, S.H.[Shao-Hui],
Wang, Y.[Yi],
Zhang, Y.F.[Yi-Fan],
Zhan, D.[Duo],
WHANet:Wavelet-Based Hybrid Asymmetric Network for Spectral
Super-Resolution From RGB Inputs,
MultMed(27), 2025, pp. 414-428.
IEEE DOI
2501
Wavelet transforms, Hyperspectral imaging, Image reconstruction,
Wavelet domain, Feature extraction, Discrete wavelet transforms,
fast fourier loss (FFL)
BibRef
Yao, Z.Y.[Zhi-Yang],
Liu, S.Y.[Shu-Yang],
Yuan, X.Y.[Xiao-Yun],
Fang, L.[Lu],
SPECAT: SPatial-spEctral Cumulative-Attention Transformer for
High-Resolution Hyperspectral Image Reconstruction,
CVPR24(25368-25377)
IEEE DOI Code:
WWW Link.
2410
Optical filters, Transformer cores, Feature extraction,
Transformers, Computational efficiency, System-on-chip, Cumulative-Attention
BibRef
Yu, Y.[Yang],
Pan, E.[Erting],
Wang, X.[Xinya],
Wu, Y.H.[Yu-Heng],
Mei, X.G.[Xiao-Guang],
Ma, J.Y.[Jia-Yi],
Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-Based
Hyperspectral Image Synthesis,
CVPR24(9297-9306)
IEEE DOI
2410
Scene classification, Graphical models, Image synthesis,
Instruments, Filling, Reliability,
Multi-source data
BibRef
Dong, X.Y.[Xiao-Yi],
Zhu, Y.[Yu],
Li, C.H.[Cheng-Hua],
Wang, P.S.[Pei-Song],
Cheng, J.[Jian],
Rispnet: A Network for Reversed Image Signal Processing,
AIM22(445-457).
Springer DOI
2304
RGB to RAW data.
BibRef
Zou, B.[Beiji],
Zhang, Y.[Yue],
Learned Reverse ISP with Soft Supervision,
AIM22(489-506).
Springer DOI
WWW Link.
2304
BibRef
Liu, S.[Song],
Li, H.W.[Hai-Wei],
Zhang, G.[Geng],
Hu, B.L.[Bing-Liang],
Chen, J.Y.[Jun-Yu],
Using Hyperspectral Reconstruction for Multispectral Images Change
Detection,
ICIVC22(183-188)
IEEE DOI
2301
Training, Image segmentation, Reconstruction algorithms,
Image reconstruction, Hyperspectral imaging, change detection,
hyperspectral image reconstruction
BibRef
Huang, J.R.[Jun-Ru],
Sun, Y.[Yubao],
Wen, J.X.[Jia-Xuan],
Liu, Q.S.[Qing-Shan],
Transformer-based Residual Network for Hyperspectral Snapshot
Compressive Reconstruction,
ICPR22(5075-5081)
IEEE DOI
2212
Image coding, Imaging, Reconstruction algorithms,
Transformer cores, Transformers, Convolutional neural networks,
Transformer joint residual block
BibRef
Shukla, A.[Ankit],
Upadhyay, A.[Avinash],
Sharma, M.[Manoj],
Chinnusamy, V.[Viswanathan],
Kumar, S.[Sudhir],
High-Resolution NIR Prediction from RGB Images:
Application to Plant Phenotyping,
ICIP22(4058-4062)
IEEE DOI
2211
Learning systems, Spectroscopy, Image registration,
Plants (biology), Superresolution, Predictive models, Pix-to-pix
BibRef
Zhang, X.Y.[Xuan-Yu],
Zhang, Y.B.[Yong-Bing],
Xiong, R.Q.[Rui-Qin],
Sun, Q.[Qilin],
Zhang, J.[Jian],
HerosNet: Hyperspectral Explicable Reconstruction and Optimal
Sampling Deep Network for Snapshot Compressive Imaging,
CVPR22(17511-17520)
IEEE DOI
2210
Photography, Deep learning, Correlation, Fuses, Sensors,
Iterative methods, Low-level vision,
Computational photography
BibRef
Yang, J.C.[Jin-Cheng],
Wang, L.S.[Li-Shun],
Cao, M.[Miao],
Wang, H.[Huan],
Zhao, Y.P.[Yin-Ping],
Yuan, X.[Xin],
Coarse-Fine Spectral-Aware Deformable Convolution for Hyperspectral
Image Reconstruction,
ICIP24(1-7)
IEEE DOI
2411
Convolution, Computational modeling, Imaging, Apertures,
Transformers, Computational efficiency,
spectral similarities
BibRef
Cai, Y.H.[Yuan-Hao],
Lin, J.[Jing],
Hu, X.W.[Xiao-Wan],
Wang, H.Q.[Hao-Qian],
Yuan, X.[Xin],
Zhang, Y.[Yulun],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Coarse-to-Fine Sparse Transformer for Hyperspectral Image
Reconstruction,
ECCV22(XVII:686-704).
Springer DOI
2211
BibRef
Earlier:
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral
Image Reconstruction,
CVPR22(17481-17490)
IEEE DOI
2210
Photography, Computational modeling, Memory management, Apertures,
Transformers, Low-level vision, Computational photography
BibRef
Cai, Y.H.[Yuan-Hao],
Lin, J.[Jing],
Lin, Z.[Zudi],
Wang, H.Q.[Hao-Qian],
Zhang, Y.[Yulun],
Pfister, H.[Hanspeter],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral
Reconstruction,
NTIRE22(744-754)
IEEE DOI
2210
Conferences, Memory management, Transformers, Image restoration,
Data mining, Convolutional neural networks
BibRef
Agarla, M.[Mirko],
Bianco, S.[Simone],
Buzzelli, M.[Marco],
Celona, L.[Luigi],
Schettini, R.[Raimondo],
Fast-n-Squeeze: towards real-time spectral reconstruction from RGB
images,
NTIRE22(1131-1138)
IEEE DOI
2210
Training, Measurement, Image resolution, Neural networks,
Real-time systems, Convolutional neural networks
BibRef
Zhu, Z.Y.[Zhi-Yu],
Liu, H.[Hui],
Hou, J.H.[Jun-Hui],
Zeng, H.Q.[Huan-Qiang],
Zhang, Q.F.[Qing-Fu],
Semantic-embedded Unsupervised Spectral Reconstruction from Single
RGB Images in the Wild,
ICCV21(2259-2268)
IEEE DOI
2203
Training, Degradation, Visualization, Semantics, Estimation,
Reconstruction algorithms, Cameras, Computational photography,
Image and video synthesis
BibRef
Li, K.[Ke],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Hyperspectral Image Super-Resolution with RGB Image Super-Resolution
as an Auxiliary Task,
WACV22(4039-4048)
IEEE DOI
2202
Training, Deep learning, Convolutional codes, Superresolution,
Neural networks, Training data, Multitasking,
Infrared/Spectral Imaging Image Processing
BibRef
Yang, L.T.[Liu-Tao],
Li, Z.N.[Zhong-Nian],
Pei, Z.X.[Zong-Xiang],
Zhang, D.Q.[Dao-Qiang],
Fs-Net: Filter Selection Network for Hyperspectral Reconstruction,
ICIP21(2933-2937)
IEEE DOI
2201
Optical filters, Training, Artificial neural networks,
Computational complexity, Image reconstruction, Optimization,
sparse regularization
BibRef
Kinoshita, Y.[Yuma],
Kiya, H.[Hitoshi],
Separated-Spectral-Distribution Estimation Based on Bayesian
Inference with Single RGB Camera,
ICIP21(1379-1383)
IEEE DOI
2201
Reflectivity, Sensitivity, Image color analysis, Estimation,
Lighting, Cameras, Robustness, Bayesian inference, spectral distribution
BibRef
Yamawaki, K.[Kazuhiro],
Yorimoto, K.[Kouhei],
Han, X.H.[Xian-Hua],
Hyperspectral Reconstruction Using Auxiliary RGB Learning from a
Snapshot Image,
ICIP22(186-190)
IEEE DOI
2211
BibRef
Earlier: A2, A3, Only:
HyperMixNet: Hyperspectral Image Reconstruction with Deep Mixed
Network from a Snapshot Measurement,
PBDL21(1184-1193)
IEEE DOI
2112
Training, Image sensors, Image coding, Sensitivity, Sensors,
Task analysis, Hyperspectral image reconstruction,
intermediate feature fusion.
Convolutional codes, Benchmark testing, Visual effects, Loss measurement
BibRef
Aslahishahri, M.[Masoomeh],
Stanley, K.G.[Kevin G.],
Duddu, H.[Hema],
Shirtliffe, S.[Steve],
Vail, S.[Sally],
Bett, K.[Kirstin],
Pozniak, C.[Curtis],
Stavness, I.[Ian],
From RGB to NIR: Predicting of near infrared reflectance from visible
spectrum aerial images of crops,
CVPPA21(1312-1322)
IEEE DOI
2112
Reflectivity, Training, Spectroscopy, Software algorithms, Crops,
Cameras, Radiometry
BibRef
Zhang, S.P.[Shi-Peng],
Wang, L.Z.[Li-Zhi],
Zhang, L.[Lei],
Huang, H.[Hua],
Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction,
CVPR21(12001-12010)
IEEE DOI
2111
Deep learning, Tensors,
Correlation, Iterative algorithms
BibRef
Sun, B.[Bo],
Yan, J.C.[Jun-Chi],
Zhou, X.[Xiao],
Zheng, Y.Q.[Yin-Qiang],
Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction
from RGB,
CVPR21(84-93)
IEEE DOI
2111
Reflectivity, Deep learning, Lighting,
Reconstruction algorithms, Filtering algorithms, Cameras
BibRef
Yamawaki, K.[Kazuhiro],
Han, X.H.[Xian-Hua],
Lightweight Hyperspectral Image Reconstruction Network with Deep
Feature Hallucination,
MLCSA22(170-184).
Springer DOI
2307
BibRef
Kohei, Y.[Yorimoto],
Han, X.H.[Xian-Hua],
Deep Residual Attention Network for Hyperspectral Image
Reconstruction,
ICPR21(8547-8553)
IEEE DOI
2105
Deep learning, Inverse problems, Benchmark testing,
Reconstruction algorithms, Visual effects, Convolutional neural networks
BibRef
Simonetto, A.[Adriano],
Zanuttigh, P.[Pietro],
Parret, V.[Vincent],
Sartor, P.[Piergiorgio],
Gatto, A.[Alexander],
Semi-supervised Deep Learning Techniques for Spectrum Reconstruction,
ICPR21(7767-7774)
IEEE DOI
2105
Deep learning, Training, Databases, Transfer learning, Estimation,
Training data, Semisupervised learning
BibRef
Cheng, N.[Niankai],
Huang, H.[Hua],
Zhang, L.[Lei],
Wang, L.Z.[Li-Zhi],
Snapshot Hyperspectral Imaging Based on Weighted High-order Singular
Value Regularization,
ICPR21(1267-1274)
IEEE DOI
2105
Solid modeling, Tensors, Correlation,
Reconstruction algorithms, Optimization
BibRef
Peng, H.[Hao],
Chen, X.M.[Xiao-Mei],
Zhao, J.[Jie],
Residual Pixel Attention Network for Spectral Reconstruction from RGB
Images,
NTIRE20(2012-2020)
IEEE DOI
2008
Image reconstruction, Hyperspectral imaging, Task analysis,
Spatial resolution, Convolution
BibRef
Fubara, B.J.,
Sedky, M.,
Dyke, D.,
RGB to Spectral Reconstruction via Learned Basis Functions and
Weights,
NTIRE20(1984-1993)
IEEE DOI
2008
Hyperspectral imaging, Image reconstruction, Machine learning,
Training, Image color analysis, Sensors
BibRef
Lin, Y.,
Finlayson, G.D.,
Physically Plausible Spectral Reconstruction from RGB Images,
NTIRE20(2257-2266)
IEEE DOI
2008
Image color analysis, Image reconstruction,
Hyperspectral imaging, Sensitivity, Cameras, Computational modeling
BibRef
Rout, L.,
Misra, I.,
Moorthi, S.M.,
Dhar, D.,
S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for
Multi-Spectral Band Synthesis,
EarthVision20(727-736)
IEEE DOI
2008
Spatial resolution, Signal resolution, Generators,
Remote sensing, Laplace equations
BibRef
Zhao, Y.,
Po, L.,
Yan, Q.,
Liu, W.,
Lin, T.,
Hierarchical Regression Network for Spectral Reconstruction from RGB
Images,
NTIRE20(1695-1704)
IEEE DOI
2008
Image reconstruction, Hyperspectral imaging, Training, Cameras, Image resolution
BibRef
Li, J.,
Wu, C.,
Song, R.,
Li, Y.,
Liu, F.,
Adaptive Weighted Attention Network with Camera Spectral Sensitivity
Prior for Spectral Reconstruction from RGB Images,
NTIRE20(1894-1903)
IEEE DOI
2008
Image reconstruction, Adaptive systems, Adaptation models,
Correlation, Hyperspectral imaging, Cascading style sheets, Task analysis
BibRef
Nie, S.J.[Shi-Jie],
Gu, L.[Lin],
Zheng, Y.Q.[Yin-Qiang],
Lam, A.[Antony],
Ono, N.[Nobutaka],
Sato, I.[Imari],
Deeply Learned Filter Response Functions for Hyperspectral
Reconstruction,
CVPR18(4767-4776)
IEEE DOI
1812
Image reconstruction, Hyperspectral imaging, Cameras, Convolution, Hardware
BibRef
Ma, J.W.[Jia-Wei],
Liu, X.Y.[Xiao-Yang],
Shou, Z.[Zheng],
Yuan, X.[Xin],
Deep Tensor ADMM-Net for Snapshot Compressive Imaging,
ICCV19(10222-10231)
IEEE DOI
2004
computational complexity, data compression, decoding,
gradient methods, image coding, image reconstruction,
noise figure 2.5 dB
BibRef
Wu, J.,
Aeschbacher, J.,
Timofte, R.,
In Defense of Shallow Learned Spectral Reconstruction from RGB Images,
PBVDL17(471-479)
IEEE DOI
1802
Dictionaries, Hyperspectral imaging, Image reconstruction,
Spatial resolution, Training
BibRef
Jia, Y.[Yan],
Zheng, Y.Q.[Yin-Qiang],
Gu, L.[Lin],
Subpa-Asa, A.[Art],
Lam, A.[Antony],
Sato, Y.[Yoichi],
Sato, I.[Imari],
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping,
ICCV17(4715-4723)
IEEE DOI
1802
Reconstruct hyperspectral data from only RGB.
cameras, geophysical image processing, image classification,
image colour analysis, image reconstruction, image sensors,
BibRef
Hoffer, N.N.[Nirit Nussbaum],
Michaeli, T.[Tomer],
Multispectral Reconstruction From Reference Objects in the Scene,
PBDL19(4350-4358)
IEEE DOI
2004
cameras, hyperspectral imaging, image reconstruction,
inverse problems, optical transfer function,
Color reconstruction
BibRef
Han, X.,
Shi, B.,
Zheng, Y.,
Residual HSRCNN:
Residual Hyper-Spectral Reconstruction CNN from an RGB Image,
ICPR18(2664-2669)
IEEE DOI
1812
Spatial resolution, Image reconstruction, Signal resolution,
Computer architecture, Cameras, Visualization
BibRef
Koundinya, S.,
Sharma, H.,
Sharma, M.,
Upadhyay, A.,
Manekar, R.,
Mukhopadhyay, R.,
Karmakar, A.,
Chaudhury, S.,
2D-3D CNN Based Architectures for Spectral Reconstruction from RGB
Images,
Restoration18(957-9577)
IEEE DOI
1812
Hyperspectral imaging, Image reconstruction,
Kernel, Feature extraction, Image resolution
BibRef
Stiebei, T.,
Köppers, S.,
Seltsam, P.,
Merhof, D.,
Reconstructing Spectral Images from RGB-Images Using a Convolutional
Neural Network,
Restoration18(1061-10615)
IEEE DOI
1812
Pattern recognition.
BibRef
Li, H.,
Xiong, Z.,
Shi, Z.,
Wang, L.,
Liu, D.,
Wu, F.,
HSVCNN: CNN-Based Hyperspectral Reconstruction from RGB Videos,
ICIP18(3323-3327)
IEEE DOI
1809
Videos, Image reconstruction, Motion compensation, Correlation,
Hyperspectral imaging, Optical imaging, Adaptive optics,
temporal-adaptive fusion
BibRef
Shi, Z.,
Chen, C.,
Xiong, Z.,
Liu, D.,
Wu, F.,
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images,
Restoration18(1052-10528)
IEEE DOI
1812
Hyperspectral imaging, Image reconstruction, Task analysis,
Spatial resolution, Cameras
BibRef
Xiong, Z.,
Shi, Z.,
Li, H.,
Wang, L.,
Liu, D.,
Wu, F.,
HSCNN:
CNN-Based Hyperspectral Image Recovery from Spectrally
Undersampled Projections,
PBVDL17(518-525)
IEEE DOI
1802
Hyperspectral imaging, Image reconstruction, Machine learning,
Optical filters, Spatial resolution
BibRef
Blasinski, H.[Henryk],
Farrell, J.[Joyce],
Wandell, B.[Brian],
An iterative algorithm for spectral estimation with spatial smoothing,
ICIP15(936-940)
IEEE DOI
1512
ADMM, Multispectral imaging, spectral reconstruction
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Parmar, M.[Manu],
Lansel, S.[Steven],
Wandell, B.A.[Brian A.],
Spatio-spectral reconstruction of the multispectral datacube using
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ICIP08(473-476).
IEEE DOI
0810
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Connah, D.,
Hardeberg, J.Y.,
Westland, S.,
Comparison of linear spectral reconstruction methods for multispectral
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ICIP04(III: 1497-1500).
IEEE DOI
0505
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Restoration from Blurred Images, Motion Blur .