Official Pytorch Implementation of Non-uniform Motion Blur Kernel Estimation via Adaptive Decomposition [ArXiv]
git clone https://github.com/GuillermoCarbajal/NonUniformBlurKernelEstimationViaAdaptiveBasisDecomposition
Model can be downloaded from here
python compute_kernels.py -i image_path -m model_path
python image_deblurring.py -b blurry_img_path --reblur_model model_path --output_folder results
Additional options:
--blurry_images: may be a singe image path or a .txt with a list of images.
--n_iters: number of iterations in the RL optimization (default 30)
--resize_factor: input image resize factor (default 1)
--saturation_method: 'combined' or 'basic'. When 'combined' is passed RL in the presence of saturated pixels is applied. Otherwise, simple RL update rule is applied in each iteration. For Kohler images, 'basic' is applied. For RealBlur images 'combined' is better.
--gamma_factor: gamma correction factor. By default is assummed gamma_factor=2.2. For Kohler dataset images gamma_factor=1.0.
If you use this code for your research, please cite our paper Non-uniform Motion Blur Kernel Estimation via Adaptive Decomposition:
@article{carbajal2021single,
title={Non-uniform Motion Blur Kernel Estimation via Adaptive Decomposition},
author={Carbajal, Guillermo and Vitoria, Patricia and Delbracio, Mauricio and Mus{\'e}, Pablo and Lezama, Jos{\'e}},
journal={arXiv e-prints},
pages={arXiv--2102},
year={2021}
}
GC was supported partially by Agencia Nacional de Investigacion e Innovación (ANII, Uruguay) ´grant POS FCE 2018 1 1007783 and PV by the MICINN/FEDER UE project under Grant PGC2018- 098625-B-I0; H2020-MSCA-RISE-2017 under Grant 777826 NoMADS and Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). The experiments presented in this paper were carried out using ClusterUY (site: https://cluster.uy) and GPUs donated by NVIDIA Corporation. We also thanks Juan F. Montesinos for his help during the experimental phase.