This repository is for DBIN and EDBIN introduced in the following papers：
[1] Wu Wang, Weihong Zeng, Yue Huang, Xinghao Ding and John Paisley, "Deep Blind Hyperspectral Image Fusion", ICCV 2019
[2] Wu Wang, Weihong Zeng, Liyan Sun, Ronghui Zhan, Yue Huang, and Xinghao Ding, "Enhanced Deep Blind Hyperspectral Image Fusion", TNNLS 2021 (The code of EDBIN will be available soon）
The code is built on Tensorflow and tested on Ubuntu 14.04/16.04 environment (Python3.6, CUDA8.0, cuDNN5.1) with 1080Ti GPUs. If you have any issues, please feel free to contact me. My mail : 23320170155546@stu.xmu.edu.cn
Hyperspectral image fusion (HIF) reconstructs high spatial resolution hyperspectral images from low spatial resolution hyperspectral images and high spatial resolution multispectral images. Previous works usually assume that the linear mapping between the point spread functions of the hyperspectral camera and the spectral response functions of the conventional camera is known. This is unrealistic in many scenarios. We propose a method for blind HIF problem based on deep learning, where the estimation of the observation model and fusion process are optimized iteratively and alternatingly during the super-resolution reconstruction. In addition, the proposed framework enforces simultaneous spatial and spectral accuracy. Using three public datasets, the experimental results demonstrate that the proposed algorithm outperforms existing blind and nonblind methods.
For the CAVE dataset, we first convert the image to .mat format, and then generate the tfrecord file, which can improve the data reading speed. For the CAVE, Harvard, and NTR2018 data sets, we split the image into 64×64 image blocks without any data augmentation. Unlike the normalization of natural images, we normalize each spectrum of each image to 0 to 1, because some spectral values are very close. You can download the tfrecord file of CAVE dataset from BaiduPan, the file extraction code is "psm1". To train the EDBIN，please run "train_cave_edbin.py".
At the time of testing, we also first converted the image into a tfrecord file. When calculating PSNR, SSIM, SAM, and ERGAS, we used the same code as DHSIS（Deep Hyperspectral Image Sharpening）, here we thank the code provided by Renwei Dian.
Our implementation of CARAFE is based on the pytorch version of XiaLiPKU， thanks for their wonderful work. The spectral normlization is based on the implementation of taki0112. We have verified that SN is beneficial to both supervised and unsupervised HIF.
Wang, W.; Zeng, W.; Huang, Y.; Ding, X.; and Paisley, J. 2019. Deep Blind Hyperspectral Image Fusion. In Proceedings of the IEEE International Conference on Computer Vision, 4150–4159.
Wang, W.; Fu, X.; Zeng, W.; Sun, L.; Zhan, R.; Huang, Y.; and Ding, X. 2021. Enhanced Deep Blind Hyperspectral Image Fusion. IEEE Transactions on Neural Networks and Learning Systems, 1–11