This repository contains an official implementation of the following CVPR 2021 paper:

Sanghyun Son and Kyoung Mu Lee, "SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation."

This repository is tested under:

• Ubuntu 18.04
• CUDA 10.1 (Compute Capability 7.5)
• >= PyTorch 1.6

We have found some issues with CUDA 10.0 version. Please use a proper CUDA version for building this repository.

Also, please install the following CUDA extension. More details are described in this repository.

$ git clone https://github.com/sanghyun-son/pysrwarp
$ cd pysrwarp
$ make

We also recommend using the attached environment.yaml file to easily set up the environment.

$ conda env create --file environment.yaml
$ conda activate srwarp

Browse to the pretrained directory and download the pretrained checkpoints by following:

$ bash download.sh

Otherwise, download them manually. Please note that those files are ~3x larger than the original model due to the saved optimizers.

Browse to the src directory and run interactive.py to open an interactive session. We note that this requires a large amount of GPU memory, especially with the MRDB backbone. We will release a memory-friendly version soon.

$ cd src
# With the MDSR backbone
$ python interactive.py --pretrained ../pretrained/srwarp_mdsr-c66c4715.ckpt
# With the MRDB backbone
$ python interactive.py --pretrained ../pretrained/srwarp_mrdb-908878d3.ckpt --backbone mrdb

# Use --img [image_path] to test your own image.

You can freely drag blue dots around the image for real-time interaction. Type 2 and 3 repeatedly to compare our results with the conventional interpolation-based warping algorithm.

We also provide some scripts to reproduce several experiments in our paper. For baseline methods in Table 3, please prepare SR results from existing methods first. Then, use the following scripts:

$ python main.py --dtest srwarp.demo --dpath $1 --data_path_test $2 --trainer srwarp.cv2 --loss loss/srwarp.txt --save srwarp/edsr_x4_cv2 --cv2_interpolation bicubic --cv2_naive --scale 4 --test_only

$1: Path to the DIV2K validation HR images.
$2: Path to the 100 SR results on DIV2K validation dataset and the corresponding forward transformation matrices (`.pth`).

An example directory structure of `$2`
|-- 0801.pth
|-- 0801_x4.png
|-- 0802.pth
|-- 0802_x4.png
`-- ...

Please download the DIV2KW datasets from the links below

• DIV2KW (Validation)

  • Input
  • GT

• DIV2KW (Test)

  • Input
  • For test ground-truth images, please use DIV2K_valid_HR dataset.

Each dataset contains 100 test inputs and corresponding 100 forward transformation matrices (.pth).

We are currently reorganizing the code. The training script will be released soon!

If you find our paper and repository useful in your research, please use the following BibTeX form:

@inproceedings{
  title={{SRW}arp: Generalized Image Super-Resolution under Arbitrary Transformation},
  author={Son, Sanghyun and Lee, Kyoung Mu},
  booktitle={CVPR},
  year={2021}
}