AdaBM

This repository includes the official implementation of the paper AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution (CVPR2024).

Requirements

A suitable conda environment named adabm can be created and activated with:

conda env create -f environment.yaml
conda activate adabm

Preparation

Dataset

For training, we use LR images sampled from DIV2K.
For testing, we use benchmark datasets and large input datasets Test2K,4K,8K. Test8K contains the images (index 1401-1500) from DIV8K. Test2K/4K contain the images (index 1201-1300/1301-1400) from DIV8K which are downsampled to 2K and 4K resolution. After downloading the datasets, the dataset directory should be organized as follows:

datasets
  -DIV2K
    - DIV2K_train_LR_bicubic # for training
    - DIV2K_train_HR
    - test2k # for testing
    - test4k
    - test8k
  -benchmark # for testing

Pretrained Models

Please download the pretrained models from here and place them in pretrained_model.

Usage

How to train

sh run.sh edsr 0 6 8 # gpu_id a_bit w_bit 
sh run.sh edsr 0 4 4 # gpu_id a_bit w_bit

How to test

sh run.sh edsr_eval 0 6 8 # gpu_id a_bit w_bit 
sh run.sh edsr_eval 0 4 4 # gpu_id a_bit w_bit

set --dir_data to the directory path for datasets.

set --pre_train to the saved model path for testing model.

the trained model is saved in experiment directory.

set --test_own to the own image path for testing.

More running scripts can be found in run.sh.

Comments

Our implementation is based on EDSR(PyTorch).

Coming Soon...

[ ] parallel patch inference

BibTeX

If you found our implementation useful, please consider citing our paper:

@misc{hong2024adabm,
      title={AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution}, 
      author={Cheeun Hong and Kyoung Mu Lee},
      year={2024},
      eprint={2404.03296},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Contact

Email: cheeun914@snu.ac.kr

Cheeun / AdaBM

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