The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Spatial-spectral encoded compressive hyperspectral imaging
Abstract
This paper proposes a novel compressive hyperspectral (HS) imaging approach that allows for high-resolution HS images to be captured in a single image. The proposed architecture comprises three key components: spatial-spectral encoded optical camera design, over-complete HS dictionary learning and sparse-constraint computational reconstruction. Our spatial-spectral encoded sampling scheme provides a higher degree of randomness in the measured projections than previous compressive HS imaging approaches; and a robust nonlinear sparse reconstruction method is employed to recover the HS images from the coded projection with higher performance. To exploit the sparsity constraint on the nature HS images for computational reconstruction, an over-complete HS dictionary is learned to represent the HS images in a sparser way than previous representations. We validate the proposed approach on both synthetic and real captured data, and show successful recovery of HS images for both indoor and outdoor scenes. In addition, we demonstrate other applications for the over-complete HS dictionary and sparse coding techniques, including 3D HS images compression and denoising.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,451
Decoding by Linear Programming
Candes, E. J., & Tao, T. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.858979 · Cited by 7,121
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Sparse Approximate Solutions to Linear Systems
Natarajan, B. K. (1995) · SIAM Journal on Computing · doi:10.1137/s0097539792240406 · Cited by 2,783
Uncertainty principles and ideal atomic decomposition
Donoho, D. L., & Huo, X. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.959265 · Cited by 1,949
Probing the Pareto Frontier for Basis Pursuit Solutions
van den Berg, E., & Friedlander, M. P. (2009) · SIAM Journal on Scientific Computing · doi:10.1137/080714488 · Cited by 1,939
Compressed sensing with coherent and redundant dictionaries
Candès, E. J., Eldar, Y. C., Needell, D., & Randall, P. (2011) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.10.002 · Cited by 898
Optimized Projections for Compressed Sensing
Elad, M. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.900760 · Cited by 835
Cited by
Rank Minimization for Snapshot Compressive Imaging
Liu, Y., Yuan, X., Suo, J., Brady, D. J., & Dai, Q. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2873587 · Cited by 466
Self-citationMask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
Cai, Y., Lin, J., Hu, X., Wang, H., Yuan, X., Zhang, Y., Timofte, R., & Van Gool, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01698 · Cited by 353
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images
Shi, Z., Chen, C., Xiong, Z., Liu, D., & Wu, F. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00139 · Cited by 339
High-quality hyperspectral reconstruction using a spectral prior
Choi, I., Jeon, D. S., Nam, G., Gutierrez, D., & Kim, M. H. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130810 · Cited by 320
Spectral imaging with deep learning
Huang, L., Luo, R., Liu, X., & Hao, X. (2022) · Light: Science & Applications · doi:10.1038/s41377-022-00743-6 · Cited by 283
Metadata
- Authors
- Lin, X., Liu, Y., Wu, J., & Dai, Q.
- Venue
- ACM Transactions on Graphics
- Published
- 2014-11-19
- Volume
- 33
- Issue
- 6
- Pages
- 1-11
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2170608472
- Cited by
- 309
- References
- 26
- FWCI
- 14.24
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Spatial-spectral encoded compressive hyperspectral imaging. https://app.cochat.ai/papers/10.1145/2661229.2661262
- The paper
- Lin, X., Liu, Y., Wu, J., & Dai, Q. (2014). Spatial-spectral encoded compressive hyperspectral imaging. ACM Transactions on Graphics. https://doi.org/10.1145/2661229.2661262
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
