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.
A feature descriptor robust to different types of image blur was proposed in @cite_13 . The descriptor is build by considering integral projections along different angular directions. According to the paper, the method outperforms the traditional descriptors. However, it still uses a feature detector that is not robust to motion blur. We argue in favor of deblurring, which allows us to use the existing feature detectors and descriptors that are known to perform well under various conditions.
@cite_13
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Feature detection and description are key ingredients of common image processing and computer vision applications. Most existing algorithms focus on...