Learning Blind Motion Deblurring

TL;DR

This paper introduces a novel recurrent neural network architecture for blind motion deblurring in videos, leveraging a new method to generate realistic training data, and demonstrates its effectiveness on real-world examples.

Contribution

The work presents a new recurrent network architecture for video deblurring and a method to produce realistic training data without extensive ground-truth requirements.

Findings

Effective deblurring of real-world blurry videos

Versatile handling of arbitrary input sizes

Superior performance compared to existing methods

Abstract

As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot frequently yields a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the motion blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp image or video. Solutions for blind deconvolution based on neural networks rely on a massive amount of ground-truth data which is hard to acquire. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel recurrent network architecture to deblur frames taking temporal information into account, which can efficiently handle arbitrary spatial and temporal input sizes. We demonstrate the versatility of our approach in a comprehensive comparison on a number of challening real-world examples.