Calibrated Hyperspectral Image Reconstruction via Graph-based Self-Tuning Network.

Related tags

Deep Learning mask_uncertainty_spectral_SCI
Overview

mask-uncertainty-in-HSI

This repository contains the testing code and pre-trained models for the paper Calibrated Hyperspectral Image Reconstruction via Graph-based Self-Tuning Network.

Requirements

  • Python 3.7.10
  • Pytorch 1.9.1
  • Numpy 1.21.2
  • Scipy 1.7.1

Test

Ten simulation testing HSI (256x256x28) are provided. Testing trials can be determined by specify trial_num

To test a pre-trained model under miscalibration many-to-many, specify mode as many_to_many, last_train as 661.

To test a pre-trained model under miscalibration one-to-many, specify mode as one_to_many, last_train as 662.

To test a pre-trained model under traditional setting one-to-one, specify mode as one_to_one, last_train as 662.

Run

python test.py

Structure of directories

directory description
Data Ten simulation testing HSIs and two real masks for testing (256x256 and 660x660)
test testing script
utils utility functions
tools model components
ssim_torch function for computing SSIM
many_to_many model structure and model checkpoint for miscalibration (many-to-many)
one_to_many model structure and model checkpoint for miscalibration (one-to-many)
one_to_one model structure and model checkpoint for traditional setting (one-to-one)

mask_uncertainty_spectral_SCI

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