Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues

This project is the official implementation of paper Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues.

Supplementart Material for Granular-DQ https://github.com/MmmingS/Granular-DQ/blob/master/Supplementary_Material_Granular_DQ.pdf

🔥🔥🔥 News

• 2024-12-16: Code release!
• 2024-12-10: Our paper has been accepted by AAAI2025!!

Requirements

cd Granular-DQ
conda env create -f environment.yaml
conda activate GDQ

Preparation

Datasets

• 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

Usage

Inference

#We provide script to test our SRResNet x2
cd src
#Change device id and the diretory to your Datasets
sh test.sh

Citation

If you find Granular-DQ useful or relevant to your project and research, please kindly cite our paper:)

@misc{wang2024thinking,
  title={Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues},
  author={Wang, Mingshen and Zhang, Zhao and Li, Feng and Xu, Ke and Miao, Kang and Wang, Meng},
  journal={arXiv preprint arXiv:2409.14330},
  year={2024}
}

Comments

Our implementation is based on EDSR(PyTorch), CADyQ, QuantSR, SwinIR, BasicSR. Thanks for these excellent works!