Pyramidal Edge-Maps and Attention Based Guided Thermal Super-Resolution

Authors: Honey Gupta and Kaushik Mitra, IIT Madras

For queries, contact: hn.gpt1@gmail.com

Installation

Required packages:

pip install -r requirements.txt

Preparing the data

Create a test.csv file that contains a list of the input images and the corresponding guide images and place it in the input folder for inside the method directories. You can use the create_dataset.py script to create the csv files.

To replicate our results, extract multi-level edge-maps from an RGB using the code for Richer Convolutional Features for Edge Detection.

A sample .csv file can be found in the input folder for the images stored in the datasets folder.

Checkpoint

Model trained on the cats model for 8x super-resolution can be downloaded here

Code Usage

Sample testing scripts:

python main.py --checkpoint_dir=checkpoint/cats --log_dir=output/cats_test --config_filename=configs/test.json  

Note: Make sure to modify the configs/test.json with the appropriate dataset

The results will be stored in --log_dir folder in the form of a html file. The 'imgs' folder will contain all the raw inputs and outputs.

More details on the training and testing procedure coming soon!

Cite Us

@inproceedings{gupta2020pyramidal,
  title={Pyramidal Edge-Maps and Attention Based Guided Thermal Super-Resolution},
  author={Gupta, Honey and Mitra, Kaushik},
  booktitle={European Conference on Computer Vision},
  pages={698--715},
  year={2020},
  organization={Springer}
}