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TRQ3DNet

The implementation of paper "TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for Hyperspectral Image Denoising"

Introduction

We propose a new deep neural network termed TRQ3DNet which combines convolutional neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network consists of two branches. One is built by 3D quasi-recurrent blocks, including convolution and quasi-recurrent pooling operation. Specifically, the 3D convolution can extract the spatial correlation within a band, and spectral correlation between different bands, while the quasi-recurrent pooling operation is able to exploit global correlation along the spectrum. The other branch is composed of a series of Uformer blocks. The Uformer block uses window-based multi-head self-attention (W-MSA) mechanism and the locally enhanced feed forward network (LeFF) to exploit the global and local spatial features. To fuse the features extracted by the two branches, we develop a bidirectional integration bridge (BI bridge) for better preserving the image feature information. Experimental results on synthetic and real HSI data show the superiority of our proposed network. For example, in the case of Gaussian noise with sigma 70, the PSNR value of our method significantly increases about 0.8 compared with other state-of-the-art methods.

Highlights

• We compare our model with other methods in Gaussian and complex denoising cases on the ICVL dataset. The results show that our model out-performs other state-of-art methods in all complex cases.

• We also evaluate our model on two real HSI datasets, i.e., Indian Pines and Urban. We adopt the visualization and one no-reference quality assessment, which evaluates the image quality according to the changes in kurtosis values of noisy images. The lower the score, the higher the quality of the recovered image. We show some visulization comparison results, from which it can be seen that our model achieves better performance than other methods.

Getting Started

1. Preparing your training/testing datasets

Here, we give an example for single scale data preparation of ICVL.

Firstly, download ICVL hyperspectral images into data/ICVL/filefolder folder and make sure your initial data are in the following structure. The train-validation-test split can be found in train_fns.txt , validation_fns_*.txt and test_fns_*.txt. (Note we split the 101 testing data into two parts for Gaussian and complex denoising respectively.) You can create the split by yourself or by the function creat_train_val_test in hsi_data.py.

data/ICVL
├── filefolder
│    └──ICVL images (*.mat)
├── train_fns.txt
├── validation_fns_gauss.txt
├── validation_fns_complex.txt
├── test_fns_gauss.txt
├── test_fns_complex.txt

Modify and run the python code hsi_data.py to split the data and create training set. Then you will get data in the following structure

data/ICVL
├── filefolder
│    ├──ICVL images (*.mat)
│    ├── trainset
│    │    └──*.mat
│    ├── valset_gauss
│    │    └──*.mat
│    ├── valset_complex
│    │    └──*.mat
│    ├── testset_gauss
│    │    └──*.mat
│    └── testset_complex
│         └──*.mat
├── trainset
│    └──ICVL64_31.npz

Modify and run the matlab code matlab/HSIValData.m and matlab/HSITestData.m to create validation and testing sets. Then you will get data in the following structure

data/ICVL
├── filefolder
├── trainset
│    └──ICVL64_31.npz
├── valset_gauss
│    └──ICVL_512_30
│        └──*.mat
│    └──ICVL_512_50
│        └──*.mat
├── valset_complex
│    └──ICVL_512_noniid
│        └──*.mat
│    └──ICVL_512_complex
│        └──*.mat
├── testset_gauss
│    └──ICVL_512_30
│        └──*.mat
│    └──ICVL_512_50
│        └──*.mat
│    └──ICVL_512_70
│        └──*.mat
│    └──ICVL_512_blind
│        └──*.mat
├── testset_complex
│    └──ICVL_512_noniid
│        └──*.mat
│    └──ICVL_512_stripe
│        └──*.mat
│    └──ICVL_512_deadline
│        └──*.mat
│    └──ICVL_512_impulse
│        └──*.mat
│    └──ICVL_512_mixture
│        └──*.mat

2. Testing with pretrained models

Read and modify basefolder and savedir in hsi_test.py.

• [Blind Gaussian noise removal]:
  python hsi_test.py -a trq3d -p gauss -r -rp ./checkpoints/trq3d/gauss/model_epoch_50_118454.pth --gpu-ids 0

• [Mixture noise removal]:
  python hsi_test.py -a trq3d -p complex -r -rp ./checkpoints/trq3d/complex/model_epoch_100_159904.pth --gpu-ids 0

The result can be found in result/trq3d/gauss/res_model_epoch_50_118454/result_ICVL_512.csv and result/trq3d/complex/res_model_epoch_100_159904/result_ICVL_512.csv.

3. Training from scratch

• Training a blind Gaussian model firstly by
  python hsi_denoising_gauss.py -a trq3d -p gauss -d ./data/ICVL/trainset/ICVL64_31.npz -v ./data/ICVL/valset_gauss --gpu-ids 0

• Using the pretrained Gaussian model as initialization to train a complex model:
  python hsi_denoising_complex.py -a trq3d -p complex -d ./data/ICVL/trainset/ICVL64_31.npz -v ./data/ICVL/valset_complex -r -rp checkpoints/trq3d/gauss/model_epoch_50_118454.pth --gpu-ids 0

Citation

Pang, L.; Gu, W.; Cao, X. TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for Hyperspectral 
Image Denoising. Remote Sens. 2022, 1, 0.

Acknowledgement

This repo is built mainly based on QRNN3D and Uformer. We thank a lot for their contributions to the community.