This repository contains the coder for the paper: λ-net: Reconstruct Hyperspectral Images from a Snapshot Measurement (International Conference on Computer Vision 2019) by Xin Miao, Xin Yuan, Yunchen Pu, Vassilis Athitsos.

The traing and testing data used in our paper is from the paper " Sparse Recovery of Hyperspectral Signal from Natural RGB Images". It can be downlaed from http://icvl.cs.bgu.ac.il/hyperspectral/. The training scene should be put in the file "training_data/",the testing scenes should be in the file "testing_data/". The data we used and the mask can be downloaded from https://drive.google.com/open?id=1lJB9Ekif4fXQSL9fGzrsxgp5YFMb7FEu.

Requirements are tensorflow, numpy, scipy.

Train the model via

main.py

The results for testing scene will be saved in 'result/model file/'. If you want to visualize the results after training the model, just run

visualize_results.py

And the PSNR value for the training and testing data can be found in

result/model file/pnsr.txt