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Fast Nearest Convolution for Real-Time Image Super-Resolution, AIM & ECCV Workshops 2022, [Paper]

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Update

[2022.11.14] A more flexible implementation of the nearest convolution initialization is provided in this issue.

[2022.11.12] We provide a simple implementation of NCNet model using PyTorch in this torch_code directory.

[2022.08.25] We have uploaded the pretrained model in Releases as V1.0

Dependencies

Performance of our Nearest Convolution

speed

Upsample methodsCPUGPUNPUPSNR
nearest23.1ms19.0ms55.0ms26.67
bilinear77.7ms21.0ms128.2ms27.67
Conv3+depth2space30.8ms26.5ms43.8ms-
NearestConv+depth2space15.9ms20.3ms14.8ms26.67

Model Training

python main.py

Then the trained keras model will be saved in ckpt/basenet/model folder.

Model Validation

python eval.py

Then the results of original keras model will be saved in original_output folder and you can calculate the validation PSNR by run python calculate_PSNR.py

Convert to TFLite

python generate_tflite.py

Then the converted tflite model will be saved in TFLite folder.

TFLite Model Validation

python test_tflite.py

Then the results of TFLite model will be saved in results folder.

Other Details

Citations

If this repo helps your research or work, please consider citing our work. The following is a BibTeX reference.

@inproceedings{luo2023fast,
  title={Fast nearest convolution for real-time efficient image super-resolution},
  author={Luo, Ziwei and Li, Youwei and Yu, Lei and Wu, Qi and Wen, Zhihong and Fan, Haoqiang and Liu, Shuaicheng},
  booktitle={Computer Vision--ECCV 2022 Workshops: Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part II},
  pages={561--572},
  year={2023},
  organization={Springer}
}

Contact

email: [ziwei.ro@gmail.com]