  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.
