Code for the paper: Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution

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Computer Vision Fusformer
Overview

Fusformer

Code for the paper: "Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution"

Plateform

Python 3.8.5 + Pytorch 1.7.1

Data

  • demo_cave.h5: A testing image from the CAVE dataset, containing "GT" ( 512x512x31), "RGB" (128x128x3) and "LRHSI" (128x128x31).
  • demo_cave_patches_h5: If your GPU memory is too small to run with the whole testing image, we cut the testing image into 64 patches with the size 64x64x31 of GT, 63x64x3 of RGB, and 16x16x31 of LRHSI, respectively.

Download link: https://github.com/J-FHu/Fusformer/releases/tag/Pytorch

Code

  • data.py: The dataloader of the training and testing data.
  • main.py: The training and testing function of our Fusformer.
  • model.py: The whole model of our Fusformer.
  • reshape2big.m : If you use the "demo_cave_patches.h5" as the testing data, the output ("PatchOutput-cave.mat") generated by the network will be 64 patches with the size of 64x64x31, which should be reshaped to the original size of 512x512x31.
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Comments
  • HSI-->MSI

    HSI-->MSI

    Thanks for sharing, it is a really great job! This paper claims that HR-MSI is obtained by Canon EOS 5D Mark II coupled with the HR-HSI. Although we can get the spectral response function of Canon EOS 5D Mark II, the band range represented by each channel of the HSI is not available. As a result, how I can get the HR-MSI? I am looking forward to your reply. Thank you very much!

    opened by ZhilingGuo 1
Releases(Pytorch)

  • Pytorch(Jan 4, 2022)

    Testing data for "Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution"

    • demo_cave.h5: A testing image from the CAVE dataset, containing "GT" ( 512x512x31), "RGB" (128x128x3) and "LRHSI" (128x128x31).
    • demo_cave_patches_h5: If your GPU memory is too small to run with the whole testing image, we cut the testing image into 64 patches with the size 64x64x31 of GT, 63x64x3 of RGB, and 16x16x31 of LRHSI, respectively.
    Source code(tar.gz)
    Source code(zip)
    demo_cave.h5(71.87 MB)
    demo_cave_patches.h5(71.87 MB)
Owner
Jin-Fan Hu (胡锦帆)
Jin-Fan Hu (胡锦帆)
GitHub
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