Fast and Robust Linear Motion Deblurring

TL;DR

This paper presents a fast, robust deconvolution method for linear motion blur in images, achieving real-time performance on standard hardware by combining Wiener filtering with iterative Richardson-Lucy deconvolution.

Contribution

It introduces an optimized algorithm combining Wiener and Richardson-Lucy deconvolution for efficient, real-time image deblurring of linear motion blur scenarios.

Findings

Real-time deblurring of 256x256 images in under 50 ms on CPU.

Effective deblurring for both 1D and 2D motion blurs.

Parallel implementations enable real-time processing on multi-core CPU and GPU.

Abstract

We investigate efficient algorithmic realisations for robust deconvolution of grey-value images with known space-invariant point-spread function, with emphasis on 1D motion blur scenarios. The goal is to make deconvolution suitable as preprocessing step in automated image processing environments with tight time constraints. Candidate deconvolution methods are selected for their restoration quality, robustness and efficiency. Evaluation of restoration quality and robustness on synthetic and real-world test images leads us to focus on a combination of Wiener filtering with few iterations of robust and regularised Richardson-Lucy deconvolution. We discuss algorithmic optimisations for specific scenarios. In the case of uniform linear motion blur in coordinate direction, it is possible to achieve real-time performance (less than 50 ms) in single-threaded CPU computation on images of $256\times256$ pixels. For more general space-invariant blur settings, still favourable computation times are obtained. Exemplary parallel implementations demonstrate that the proposed method also achieves real-time performance for general 1D motion blurs in a multi-threaded CPU setting, and for general 2D blurs on a GPU.