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.
In the context of visual SLAM, motion blur is handled in @cite_9 @cite_15 @cite_0 . @cite_9 use the information from the SLAM system to estimate the motion blur. The estimates are then used for deblurring, although not all frames are deblurred. The methods @cite_15 @cite_0 are based on a different idea. Instead of deblurring, the image patches are blurred to make the patches look similar. This may help in feature matching, but the number of detected features is usually greatly reduced in the presence of motion blur which is a problem especially when the number of distinctive features is limited. It is also worth mentioning that unlike previous methods @cite_9 @cite_15 @cite_0 , our approach, which operates on single images, is not limited to SLAM.
@cite_0 @cite_9 @cite_15
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The paper presents a method aiming at improving the reliability of Simultaneous Localization And Mapping (SLAM) approaches based on vision systems. Classical SLAM approaches treat camera capturing time as negligible, and the recorded frames as sharp and well-defined, but this hypothesis does not hold true when the camera is moving too fast. In such cases, in fact, frames may be severely degraded by motion blur, making features matching task a difficult operation. The method here presented is based on a novel approach that combines the benefits of a fully probabilistic SLAM algorithm with the basic ideas behind modern motion blur handling algorithms. Whereby the Kalman Filter, the new approach predicts the best possible blur Point Spread Function (PSF) for each feature and performs matching using also this information. Handling motion blur is one of important issues in visual SLAM. For a fast-moving camera, motion blur is an unavoidable effect and it can degrade the results of localization and reconstruction severely. In this paper, we present a unified algorithm to handle motion blur for visual SLAM, including the blur-robust data association method and the fast deblurring method. In our framework, camera motion and 3-D point structures are reconstructed by SLAM, and the information from SLAM makes the estimation of motion blur quite easy and effective. Reversely, estimating motion blur enables robust data association and drift-free localization of SLAM with blurred images. The blurred images are recovered by fast deconvolution using SLAM data, and more features are extracted and registered to the map so that the SLAM procedure can be continued even with the blurred images. In this way, visual SLAM and deblurring are solved simultaneously, and improve each other's results significantly. We consider the problem of visual tracking of regions of interest in a sequence of motion blurred images. Traditional methods couple tracking with deblurring in order to correctly account for the effects of motion blur. Such coupling is usually appropriate, but computationally wasteful when visual tracking is the lone objective. Instead of deblurring images, we propose to match regions by blurring them. The matching score for two image regions is governed by a cost function that only involves the region deformation parameters and two motion blur vectors. We present an efficient algorithm to minimize the proposed cost function and demonstrate it on sequences of real blurred images.