• Corpus ID: 180943

Learned Spectral Super-Resolution

@article{Galliani2017LearnedSS,
  title={Learned Spectral Super-Resolution},
  author={S. Galliani and Charis Lanaras and Dimitrios Marmanis and Emmanuel Baltsavias and Konrad Schindler},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.09470}
}
We describe a novel method for blind, single-image spectral super-resolution. [] Key Method Technically, we follow the current best practice and implement a convolutional neural network (CNN), which is trained to carry out the end-to-end mapping from an entire RGB image to the corresponding hyperspectral image of equal size. We demonstrate spectral super-resolution both for conventional RGB images and for multi-spectral satellite data, outperforming the state-of-the-art.
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88 Citations
Highly Influential Citations
21
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36
Methods Citations
46
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88 Citations

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A multi-scale deep convolutional neural network is presented to explicitly map the input RGB image into a hyperspectral image through symmetrically downsampling and upsampling the intermediate feature maps in a cascading paradigm, ultimately improving the spectral reconstruction accuracy.

Spectral Super-Resolution for Multispectral Image Based on Spectral Improvement Strategy and Spatial Preservation Strategy

A spectral super-resolution method to recover a high-spectral-resolution HS image from multispectral (MS) images to address issues such as low spatial resolution, low temporal resolution, and some of the acquired spectral bands are either with low signal-to- noise ratio (SNR) or invalid because of the very high-noise level.

Deep Residual Network of Spectral and Spatial Fusion for Hyperspectral Image Super-Resolution

  • Xianhua HanYenwei Chen
  • Environmental Science, Mathematics
    2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM)
  • 2019
Experimental results on benchmark datasets validate that the proposed residual CNN for hyperspectral super-resolution outperforms the state-of-the-art methods in both quantitative values and visual effect.

Spectral Superresolution of Multispectral Imagery With Joint Sparse and Low-Rank Learning

J-SLoL infers and recovers the unknown HS signals over a larger coverage by sparse coding on the learned dictionary pair, and validate the SSR performance on three HS–MS data sets in terms of reconstruction, classification, and unmixing by comparing with several existing state-of-the-art baselines, showing the effectiveness and superiority of the proposed J- SLoL algorithm.

Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network

Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR.

A Spectral–Spatial Jointed Spectral Super-Resolution and Its Application to HJ-1A Satellite Images

A spectral and spatial jointed spectral super-resolution method is proposed in this letter using an end-to-end learning strategy for each subspace with the cluster-based multibranch backpropagation neural network (BPNN).

Spectral Super-Resolution Based on Dictionary Optimization Learning via Spectral Library

The validity and superiority of the proposed SSR algorithm is confirmed by comparing it with several benchmark state-of-the-art approaches on different datasets by incorporating a spectral library as a priori in the spectral domain.

Residual HSRCNN: Residual Hyper-Spectral Reconstruction CNN from an RGB Image

A spectral reconstruction CNN for spectral super-resolution with an available RGB image, which predicts the high-frequency content of the fine spectral wavelength in narrow band interval is explored and a novel residual hyper-spectral reconstruction CNN framework is proposed to estimate the non-recovered high- frequencies from the output of the baseline CNN.

Separable-spectral convolution and inception network for hyperspectral image super-resolution

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Hyperspectral image super-resolution combining with deep learning and spectral unmixing

...

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