The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images
Abstract
Hyperspectral recovery from a single RGB image has seen a great improvement with the development of deep convolutional neural networks (CNNs). In this paper, we propose two advanced CNNs for the hyperspectral reconstruction task, collectively called HSCNN+. We first develop a deep residual network named HSCNN-R, which comprises a number of residual blocks. The superior performance of this model comes from the modern architecture and optimization by removing the hand-crafted upsampling in HSCNN. Based on the promising results of HSCNN-R, we propose another distinct architecture that replaces the residual block by the dense block with a novel fusion scheme, leading to a new network named HSCNN-D. This model substantially deepens the network structure for a more accurate solution. Experimental results demonstrate that our proposed models significantly advance the state-of-the-art. In the NTIRE 2018 Spectral Reconstruction Challenge, our entries rank the 1st (HSCNN-D) and 2nd (HSCNN-R) places on both the "Clean" and "Real World" tracks. (Codes are available at [clean-r], [realworld-r], [clean-d], and [realworld-d].)
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
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Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
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Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 7,736
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Cited by
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Cai, Y., Lin, J., Lin, Z., Wang, H., Zhang, Y., Pfister, H., Timofte, R., & Gool, L. V. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00090 · Cited by 334
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
- Shi, Z., Chen, C., Xiong, Z., Liu, D., & Wu, F.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1052-10528
- OpenAlex
- W2893739000
- Cited by
- 339
- References
- 33
- FWCI
- 98.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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