Bringing Alive Blurred Moments

Published in CVPR 2019, 2019

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.

Learning based Blur Detection and Segmentation

Published in ICIP 2018, 2018

We present a robust two-level architecture for blur-based segmentation of a single image. First network is a fully convolutional encoder-decoder for estimating a semantically meaningful blur map from the full-resolution blurred image. Second network is a CNN-based classifier for obtaining local (patch-level) blur-probabilities. Fusion of the two network outputs enables accurate blur-segmentation using Graph-cut optimization over the obtained probabilities. We also show its applications in blur magnification and matting.