Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

TL;DR

This paper introduces a real-time inertial-based deblurring method that enhances feature detection and matching in motion-blurred images, improving 3D reconstruction accuracy.

Contribution

The proposed method uniquely handles spatially-variant blur and rolling shutter distortion while operating in real-time, unlike existing algorithms.

Findings

Increases the number of detected keypoints.

Improves feature repeatability and localization accuracy.

Enhances 3D reconstruction quality.

Abstract

Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.