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.
To address the issue with motion blur, @cite_14 @cite_4 estimate the blur directly from the image. In @cite_14 , the recovered PSF, which is assumed to be spatially-invariant and linear, is used to deblur the image before feature detection and description. The method in @cite_4 relaxes the assumption of spatially-invariant blur. Instead of deblurring, the estimated PSFs are used to build an adapted Gaussian scale-space pyramid. The idea is to blur less on the direction of the motion rather than applying an isotropic Gaussian blur to all levels in the pyramid.
@cite_14 @cite_4
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We present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed by Acar and Vogel (1994). The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM) implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (PSF). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the PSF can be recovered under the presence of high noise level. Finally, we remark that PSFs without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach. Blind deconvolution of motion blur is hard, but it can be made easier if multiple images are available. This observation, and an algorithm using two differently-blurred images of a scene are the subject of this paper. While this idea is not new, existing methods have so far not delivered very practical results. In practice, the PSFs corresponding to the two given images are estimated and assumed to be close to the latent motion blurs. But in actual fact, these estimated blurs are often far from the truth, for a simple reason: They often share a common, and unidentified PSF that goes unaccounted for. That is, the estimated PSFs are themselves "blurry". While this can be due to any number of other blur sources including shallow depth of field, out of focus, lens aberrations, diffraction effects, and the like, it is also a mathematical artifact of the ill-posedness of the deconvolution problem. In this paper, instead of estimating the PSFs directly and only once from the observed images, we first generate a rough estimate of the PSFs using a robust multichannel deconvolution algorithm, and then "deconvolve the PSFs" to refine the outputs. Simulated and real data experiments show that this strategy works quite well for motion blurred images, producing state of the art results. This paper describes a visual feature detector and descriptor scheme designed to address the specific problems of humanoid robots in the tasks of visual odometry, localization, and SLAM (Simultaneous Localization And Mapping). During walking, turning, and squatting movements, the camera of a humanoid robot moves in jerky and sometimes unpredictable way. This causes an undesired motion blur in the images grabbed by the robot camera, that negatively affects the performance of the image processing algorithms. Indeed, the classical features detector and descriptor filtering techniques, that proved to work so well for wheeled robots, do not perform so reliably in humanoid robots. This paper presents a method to detect image interest points (invariant to scale transformation and rotations) robust to motion-blur introduced by the camera motion. Our approach is based on a preprocessing step to estimate the point spread function (PSF) of the motion blur. The PSF is used to deconvolve the image reducing the blur. Then, we apply a feature detector inspired by SURF approach and the feature descriptor from SIFT. Experiments performed on standard datasets corrupted with motion blur and on images taken by a camera mounted on a small humanoid robot show the effectiveness of the proposed technique. Our approach presents higher performances and higher reliability in matching features in the different images of a sequence affected by motion-blur. This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model and is thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared to existing methods. We also demonstrate improved performance on real blurred images on the FRGC 1.0 face database. Furthermore, we show and explain how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.