Official Pytorch Implementation of Blind Motion Deblurring with Pixel-Wise Kernel Estimation via Kernel Prediction Networks.
This article was submitted to IEEE Transactions On Computational Imaging. Supp. Material
The proposed motion deblurring method demonstrates strong generalization capabilities, delivering sharp image restorations on real-world photographs. In certain instances, it even surpasses the sharpness of the ground-truth images. It is worth noting that the conventional quality metric PSNR tends to favor blurred outputs. Thus, an increase in PSNR can be achieved by blurring the restoration. In contrast, employing metrics designed to gauge image sharpness, such as CPBD, aligns more closely with our perception of sharpness and offers a better assessment of the restoration quality.
git clone https://github.com/GuillermoCarbajal/J-MKPD.git
Kernels Prediction Model
Restoration Network
python image_deblurring.py -b blurry_img_path --reblur_model reblur_model_path --nimbusr_model restoration_model_path --output_folder results
Additional options:
--blurry_images: may be a singe image path or a .txt with a list of images.
--resize_factor: input image resize factor (default 1)
--gamma_factor: gamma correction factor. By default is assummed gamma_factor=2.2. For Kohler dataset images gamma_factor=1.0.
python compute_kernels.py -i image_path -m kernels_prediction_model_path
Our method generalize better to datasets not seen during training. Other methods motion fields are correlated with the image structure, suffer from the aperture problem and predict deltas on low variance regions.
Kernels Prediction Model (light streaks)
Restoration Network (light streaks)
We thank the authors of Deep Model-Based Super-Resolution with Non-Uniform Blur for the Blind Deconvolution Network provided in https://github.com/claroche-r/DMBSR
Guillermo Carbajal 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.