Bringing Alive Blurred Moments

Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6830-6839

Abstract


We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during the time of exposure. We first learn motion representation from sharp videos in an unsupervised manner through training of a convolutional recurrent video autoencoder network that performs a surrogate task of video reconstruction. Once trained, it is employed for guided training of a motion encoder for blurred images. This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder. As an intermediate step, we also design an efficient architecture that enables real-time single image deblurring and outperforms competing methods across all factors: accuracy, speed, and compactness. Experiments on real scenes and standard datasets demonstrate the superiority of our framework over the state-of-the-art and its ability to generate a plausible sequence of temporally consistent sharp frames.

Related Material


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[bibtex]
@InProceedings{Purohit_2019_CVPR,
author = {Purohit, Kuldeep and Shah, Anshul and Rajagopalan, A. N.},
title = {Bringing Alive Blurred Moments},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}