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

Last update: Dec 29, 2022

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

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

Related tags

Overview

mask-uncertainty-in-HSI

Requirements

Test

Structure of directories

mask_uncertainty_spectral_SCI

Owner

JIAMIAN WANG

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