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Bicubic++: Slim, Slimmer, Slimmest - Designing an Industry-Grade Super-Resolution Network (:rocket: Winner of NTIRE RTSR Challange Track 2 (x3 SR) @ CVPR 2023)

Bahri Batuhan Bilecen, Mustafa Ayazoglu

[Preprint] [CVF Open Access] [Presentation Video]

Abstract: We propose a real-time and lightweight single-image super-resolution (SR) network named Bicubic++. Despite using spatial dimensions of the input image across the whole network, Bicubic++ first learns quick reversible downgraded and lower resolution features of the image in order to decrease the number of computations. We also construct a training pipeline, where we apply an end-to-end global structured pruning of convolutional layers without using metrics like magnitude and gradient norms, and focus on optimizing the pruned network's PSNR on the validation set. Furthermore, we have experimentally shown that the bias terms take a considerable amount of the runtime while increasing PSNR marginally, hence we have also applied bias removal to the convolutional layers.

Our method adds ~1dB on Bicubic upscaling PSNR for all tested SR datasets and runs with ~1.17ms on RTX3090 and ~2.9ms on RTX3070, for 720p inputs and 4K outputs, both in FP16 precision. Bicubic++ won NTIRE 2023 RTSR Track 2 x3 SR competition and is the fastest among all competitive methods. Being almost as fast as the standard Bicubic upsampling method, we believe Bicubic++ can set a new industry standard.

teaser

Installation

Clone the repository.

git clone https://github.com/aselsan-research-imaging-team/bicubic-plusplus.git

Install the dependencies. We recommend using a virtual environment to manage the packages. You may use the requirements.txt provided.
- Python 3.8
- PyTorch 1.13.1
- CUDA 11.2
- Other packages (numpy==1.24.2, opencv-python=4.6.0.66, pytorch-lightning==1.6.0, pyyaml==6.0)

Test

The utilized dataset in our ablation studies and the validation steps is the 48-img DIV2K validation dataset. The image indices are provided in DIV2Kval_48img_indices.

Modify validation dataset paths (data.val.lr_path and data.val.hr_path) in configs/conf.yaml accordingly. Make sure that load_pretrained and pretrained_path are set correctly. You may change loader.val.batch_size to speed up the inference.
Run test code.
```
python test.py
```

Train

The proposed three-stage training pipeline code will be published soon. For straightforward training:

Set validation & training dataset paths in configs/conf.yaml (data.val.lr_path, data.val.hr_path, data.train.lr_path, data.train.hr_path). Set loader.train.batch_size and loader.val.batch_size according to your dataset.
Run train code.
```
python train.py
```

To-do list

Publish the pre-trained x3 PyTorch model with inference & base training scripts
Share the 48-img DIV2K JPEG Q=90 fast validation set used in the ablation studies
Provide inference scripts
Share the proposed three-stage training pipeline code
Share a cloud notebook for online inference and training
Publish C++ inference scripts and the compiled model

Citation

@InProceedings{Bilecen_2023_CVPR,
    author    = {Bilecen, Bahri Batuhan and Ayazoglu, Mustafa},
    title     = {Bicubic++: Slim, Slimmer, Slimmest - Designing an Industry-Grade Super-Resolution Network},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {1623-1632}
}

License and Acknowledgement

This work is under CC BY-NC-SA 4.0 license.