1. [Publications](/publications) 2. Reblur2Deblur: Deblurring Videos via Self-Supervised Learning # Reblur2Deblur: Deblurring Videos via Self-Supervised Learning ![](/sites/default/files/styles/wide/public/publications/reblur2deblur.png?itok=t9CAm0bk) Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that better reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the data and use it to synthesize plausible images. Their results are impressive, but they are not always faithful to the content of the latent image. We present an approach that bridges the two. Our method fine-tunes existing deblurring neural networks in a self-supervised fashion by enforcing that the output, when blurred based on the optical flow between subsequent frames, matches the input blurry image. We show that our method significantly improves the performance of existing methods on several datasets both visually and in terms of image quality metrics. ## Authors Huaijin Chen (Rice University) [Jinwei Gu](/person/jinwei-gu) Orazio Gallo (NVIDIA) [Ming-Yu Liu](/person/ming-yu-liu) Ashok Veeraraghavan (Rice University) [Jan Kautz](/person/jan-kautz) ## Publication Date Friday, May 4, 2018 ## Published in [IEEE International Conference on Computational Photography (ICCP)](http://iccp2018.ece.cmu.edu/) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computational Photography and Imaging](/research-area/computational-photography-imaging) [Computer Vision](/research-area/computer-vision) ## External Links [Arxiv](https://arxiv.org/abs/1801.05117) [Supplementary](https://goo.gl/nYPjEQ) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .