PLKSR: Partial Large Kernel CNNs for Efficient Super-Resolution

This repository is an official implementation of the paper "Partial Large Kernel CNNs for Efficient Super-Resolution", Arxiv, 2024.

by Dongheon Lee, Seokju Yun, and Youngmin Ro

[paper] [pretrained models]

Updates

[2024-08-19] PLKSR-IGConv+, capable of predicting multiple integer scales with a single model, has been released and is available in the repository.
[2024-05-22] Pre-trained models of the PLKSR on the DF2K dataset are released.
[2024-05-10] Real-PLKSR, to train PLKSR stably on real-world SISR task, has been provided. Implementation details are available in issue and you train/test it with the neosr framework.

Installation

git clone https://github.com/dslisleedh/PLKSR.git
cd PLKSR
conda create -n plksr python=3.10
conda activate plksr
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt
python setup.py develop

Train

python plksr/train.py -opt=$CONFIG_PATH

Test

python plksr/test.py -opt=$CONFIG_PATH

Results

Quantitative Results

### Main model ![image](https://github.com/dslisleedh/PLKSR/blob/main/figs/Quantitative.png) ### Tiny model ![image](https://github.com/dslisleedh/PLKSR/blob/main/figs/Quantitative_tiny.png)

Visual Results

![image](https://github.com/dslisleedh/PLKSR/blob/main/figs/Qualitative_1.png) ![image](https://github.com/dslisleedh/PLKSR/blob/main/figs/Qualitative_2.png)

Acknowledgement

This work is released under the MIT license. The codes are based on BasicSR. Thanks for their awesome works.

Contact

If you have any questions, please contact dslisleedh@gmail.com

dslisleedh / PLKSR

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