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dmbsr's Introduction

Deep Model-Based Super-Resolution with Non-uniform Blur

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This repository implements the code of Deep Model-Based Super-Resolution with Non-uniform Blur

Train

To train the code please first download COCO dataset available at: https://cocodataset.org.

python main_train.py -opt options/train_nimbusr.json

Test

Pre-trained model is available at: model_zoo/DMBSR.pth

Our blur kernels are available for download here. They need to be added in the folder |-kernels

See test_model.ipynb to test the model on COCO dataset.

Results

We achieve state-of-the-art results in super-resolution in the presence of spatially-varying blur. Here are some of the results we obtained. Feel free to test on your own sample using the testing notebook.

LR SwinIR BlindSR USRNet Ours HR
LR SwinIR BlindSR USRNet Ours

Real-world images

For this section, we used the code provided by https://github.com/GuillermoCarbajal/NonUniformBlurKernelEstimation to estimate the kernel and we combine their kernel estimation to our super-resolution model. We also use the dataset provided by "Laurent D’Andrès, Jordi Salvador, Axel Kochale, and Sabine Süsstrunk. Non-parametric blur map regression for depth of field extension".

Defocus x2 super-resolution

LR SwinIR BlindSR Ours

Deblurring

LR DMPHN RealBlur MPRNet Ours

Acknowledgement

The codes use KAIR as base. Please also follow their licenses. I would like to thank them for the amazing repository.

Citation

If you use our work, please cite us with the following:

@InProceedings{laroche2023dmbsr,
  title = {Deep Model-Based Super-Resolution with Non-Uniform Blur},
  author = {Laroche, Charles and Almansa, Andrés and Tassano, Matias},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}
  year = {2023}
}

dmbsr's People

Contributors

gti-fing avatar claroche-r avatar claroche-gpfw avatar

Stargazers

朝焼けのスターマイン avatar 温伟磊 avatar Xudong Zeng avatar Keyeh avatar  avatar An-zhi WANG avatar  avatar Utsav Akhaury avatar

Watchers

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dmbsr's Issues

flip kernel when do the convolution.

Hello,

I just noticed that you flip the blur kernel in the convolve_tensor function and do not do that in cross_correlate_tensor. May I know the reason behind this?

Thank you!

Missing file

Hello, when I reproduced your thesis, I found that a file was missing: kernels/custom_blur_centered.mat.
Is it convenient to provide this file?

test size

So the size of the image in the test phase is fixed to 256 by 256?
does it work with any bigger size, says in real application?

test_model.ipynb cant not be opened

hi, when i was trying to test the model it suggested that "expected double-quoted property name in JSON at position 72", could you please fix the problem? thanks for your help

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