Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution

@inproceedings{Wang2017DeepRC,
  title={Deep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution},
  author={Chen Wang and Yun Liu and Xiao Bai and Wenzhong Tang and Peng Lei and J. Zhou},
  booktitle={ICIG},
  year={2017}
}
Hyperspectral image is very useful for many computer vision tasks, however it is often difficult to obtain high-resolution hyperspectral images using existing hyperspectral imaging techniques. In this paper, we propose a deep residual convolutional neural network to increase the spatial resolution of hyperspectral image. Our network consists of 18 convolution layers and requires only one low-resolution hyperspectral image as input. The super-resolution is achieved by minimizing the difference… 
Pyramid Fully Convolutional Network for Hyperspectral and Multispectral Image Fusion
TLDR
A pyramid fully convolutional network made up of an encoder sub-network and a pyramid fusion sub- network to address the issue of low spatial resolution hyperspectral and high spatial resolution multispectral image fusion.
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution
TLDR
This paper proposes a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet), which can effectively explore spatial-spectral information by using 3D convolution instead of 2D Convolution, which enables the network to better extract potential information.
Hyperspectral Image Super-Resolution with 1D-2D Attentional Convolutional Neural Network
TLDR
A novel framework, i.e., a 1D–2D attentional convolutional neural network, which employs a separation strategy to extract the spatial–spectral information and then fuse them gradually, which can not only present a perfect solution for the HSISR problem, but also explore the potential in hyperspectral pansharpening.
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
TLDR
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.
Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution
TLDR
A novel mixed convolutional module (MCM) is designed to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image.
Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network
TLDR
A HSI-MSI fusion method by designing a deep convolutional neural network with two branches which are devoted to features of HSI and MSI, which demonstrates that the proposed method is competitive with other state-of-the-art fusion methods.
Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution
TLDR
This work proposes an unsupervised deep learning-based fusion method—HyCoNet—that can solve the problems in HSI–MSI fusion without the prior PSF and SRF information.
Hyperspectral–Multispectral Image Fusion Enhancement Based on Deep Learning
TLDR
This chapter presents the solution for HSI-MSI fusion based on a two-branch convolutional neural network and reviews the recent advances in HSI resolution enhancement technologies.
...
...

References

SHOWING 1-10 OF 21 REFERENCES
Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution
TLDR
Comparison of the proposed approach with the state-of-the-art methods on both ground-based and remotely-sensed public hyperspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets.
Hyperspectral Super-Resolution by Coupled Spectral Unmixing
TLDR
This paper proposes a method which performs hyperspectral super-resolution by jointly unmixing the two input images into the pure reflectance spectra of the observed materials and the associated mixing coefficients, with a number of useful constraints imposed by elementary physical properties of spectral mixing.
RGB-Guided Hyperspectral Image Upsampling
TLDR
An algorithm to enhance and upsample the resolution of hyperspectral images is presented and has outperformed state-of-the-art matrix factorization based approaches.
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation.
TLDR
A new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene to improve the accuracy of non-negative sparse coding and to exploit the spatial correlation among the learned sparse codes.
High-resolution hyperspectral imaging via matrix factorization
TLDR
This paper introduces a simple new technique for reconstructing a very high-resolution hyperspectral image from two readily obtained measurements: A lower-resolution hyper-spectral image and a high- resolution RGB image.
Statistics of real-world hyperspectral images
TLDR
Using a new collection of fifty hyperspectral images of indoor and outdoor scenes, an optimized “spatio-spectral basis” is derived for representing hyperspectrals image patches and statistical models for the coefficients in this basis are explored.
Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep
Learning a Deep Convolutional Network for Image Super-Resolution
TLDR
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
TLDR
This paper presents the first convolutional neural network capable of real-time SR of 1080p videos on a single K2 GPU and introduces an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output.
Deep Networks for Image Super-Resolution with Sparse Prior
TLDR
This paper shows that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end, and leads to much more efficient and effective training, as well as a reduced model size.
...
...