# Hand-held Video Deblurring via Efficient Fourier Aggregation

arXiv: https://arxiv.org/abs/1509.05251 · HTML: https://mlsanity.com/paper/1509.05251/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2015.

## Built on by (most cited first, 8 of 8)

- [Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes](https://mlsanity.com/paper/2108.09982/) (2021)
- [Face Deblurring using Dual Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2207.11617/) (2022)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Real-Time Video Deblurring via Lightweight Motion Compensation](https://mlsanity.com/paper/2205.12634/) (2022)
- [Efficient joint noise removal and multi exposure fusion](https://mlsanity.com/paper/2112.03701/) (2021)
- [Wavelet Burst Accumulation for turbulence mitigation](https://mlsanity.com/paper/2410.22802/) (2024)
- [Joint Video Enhancement with Deblurring, Super-Resolution, and Frame Interpolation Network](https://mlsanity.com/paper/2506.03892/) (2025)
- [Video Deblurring with Deconvolution and Aggregation Networks](https://mlsanity.com/paper/2506.04054/) (2025)

## Abstract (arXiv)

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 video frames are generally blurred differently. Experiments with numerous videos recorded in the wild, along with extensive comparisons, show that the proposed algorithm achieves state-of-the-art results while at the same time being much faster than its competitors.