Channel Split Convolutional Neural Network (ChaSNet) for Thermal ImageSuper-Resolution

The repository contains the official code for the work "Channel Split Convolutional Neural Network (ChaSNet) for Thermal ImageSuper-Resolution" accepted for PBVS-2021 workshop in-conjuction with CVPR-2021 conference.

- Description

- Result

(* x2 results are taken on LR images obtained by bicubic downscaling on MR data)

Method	x4 PSNR	x4 SSIM	x2 PSNR	x2 SSIM
Bicubic	32.66	0.8625	34.74	0.9200
SRResNet	33.12	0.9018	33.66	0.9229
MSRN	34.47	0.9076	36.96	0.9471
SRFeat	34.12	0.9007	-	-
EDSR	34.48	0.9068	36.91	0.9466
RCAN	34.42	0.9072	36.67	0.9438
TEN	33.62	0.8910	36.10	0.9392
CNN-IR	33.77	0.8938	36.66	0.9438
PBVS-2020 winner	34.49	0.9073	-	-
TherISuRNet	34.49	0.9101	36.76	0.9450
Proposed	34.86	0.9133	37.38	0.9509
Proposed+	34.90	0.9134	37.49	0.9518

- Pre-Trained models

The pre-trained model for track-2 (i.e. scaling of x2) is shared with the repository while the pre-trained model for the track-1 (i.e. scaling of x4) can be downloaded from the link.

- Training the model

To train from scratch, you need to set root directory and dataset directory into options/train/train_vkgenPSNR.json file. Then run the following command to start the training.

python train.py -opt PATH-to-json_file

- Testng the model

To test your pre-trained model, you need to set root directory and dataset directory into options/test/test_VKGen.json file. Then run the following command to start the training.

python test.py -opt PATH-to-json_file

To get the SR images using self-assemble technique, you need to run the following line of code.

python self_assemble_test.py -opt PATH-to-json_file

- Requirement of packages

The list of packages required to run the code is given in chasnet.yml file.

We are thankful to Xinntao for their ESRGAN code using which this work has been implemented. For any problem, you may contact at [email protected].