Abstract:

We present an approach for blind image deblurring, which handles non-uniform blurs. Our algorithm has two main components: (i) A new method for recovering the unknown blur-field directly from the blurry image, and (ii) A method for deblurring the image given the recovered nonuniform blur-field. Our blur-field estimation is based on analyzing the spectral content of blurry image patches by re-blurring them. Being unrestricted by any training data, it can handle a large variety of blur sizes, yielding superior blur-field estimation results compared to trainingbased deep-learning methods. Our non-uniform deblurring algorithm is based on the internal image-specific patchrecurrence prior. It attempts to recover a sharp image which, on one hand – results in the blurry image under our estimated blur-field, and on the other hand – maximizes the internal recurrence of patches within and across scales of the recovered sharp image. The combination of these two components gives rise to a blind-deblurring algorithm, which exceeds the performance of state-of-the-art CNN-based blind-deblurring by a significant margin, without the need for any training data.

Materials for download:

Paper Supplementary material Israel Vision Day talk slides (.ppsx) Deblurring results on real blur Deblurring results on synthetic moderate blur Deblurring results on synthetic severe blur

Citation:

Yuval Bahat, Netalee Efrat, Michal Irani
Non-Uniform Blind Deblurring by Reblurring
International Conference on Computer Vision (ICCV), Oct 2017, Venice, Italy

  
					
					@INPROCEEDINGS{BahatandEfrat:ICCV2017, 
					author={Yuval Bahat and
					Netalee Efrat and
					Michal Irani}, 
					booktitle={2017 IEEE International Conference on Computer Vision}, 
					title={Non-Uniform Blind Deblurring by Reblurring}, 
					year={2017},}