Hand-held Video Deblurring via Efficient Fourier Aggregation
Mauricio Delbracio, Guillermo Sapiro

TL;DR
This paper introduces a fast Fourier domain-based algorithm for removing camera shake blur from hand-held videos, effectively handling dynamic scenes with multiple moving objects and occlusions.
Contribution
The work presents a novel, efficient Fourier aggregation method for deblurring videos captured with hand-held cameras, addressing challenges of motion and occlusion.
Findings
Achieves state-of-the-art deblurring quality.
Runs significantly faster than existing methods.
Effectively handles dynamic scenes with multiple moving objects.
Abstract
Videos captured with hand-held cameras often suffer from a significant amount of blur, mainly caused by the inevitable natural tremor of the photographer's hand. In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video. The dynamic nature of typical videos with the presence of multiple moving objects and occlusions makes this problem of camera shake removal extremely challenging, in particular when low complexity is needed. Given an input video frame, we first create a consistent registered version of temporally adjacent frames. Then, the set of consistently registered frames is block-wise fused in the Fourier domain with weights depending on the Fourier spectrum magnitude. The method is motivated from the physiological fact that camera shake blur has a random nature and therefore, nearby…
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