The World of Fast Moving Objects

The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate with high angular speed. FMOs are very common in sports videos, but are not rare elsewhere. In a single frame, such objects are often barely visible and appear as semi-transparent streaks. A method for the detection and tracking of FMOs is proposed. The method consists of three distinct algorithms, which form an efficient localization pipeline that operates successfully in a broad range of conditions. We show that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance. The proposed method is evaluated on a new annotated dataset. The results show that existing trackers are inadequate for the problem of FMO localization and a new approach is required. Two applications of localization, temporal super-resolution and highlighting, are presented

Enhancing Video Deblurring using Efficient Fourier Aggregation

Video Deblurring is a process of removing blur from all the video frames and achieving the required level of smoothness. Numerous recent approaches attempt to remove image blur due to camera shake,either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem.An efficient video deblurring system to handle the blurs due to shaky camera and complex motion blurs due to moving objects has been proposed.The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method�s rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently.The proposed system has effectively deblurred the video and results showed that the reconstructed video is sharper and less noisy than the original ones.The proposed Fourier Burst Accumulation algorithm produced similar or better results than the state-of-the-art multi-image deconvolution while being significantly faster and with lower memory footprint.The method is robust to moving objects as it acquired the consistent registration scheme

Image Deblurring via an Adaptive Dictionary Learning Strategy

Recently, sparse representation has been applied to image deblurring. The dictionary is the fundamental part of it and the proper selection of dictionary is very important to achieve super performance. The global learned dictionary might achieve inferior performances since it could not mine the specific information such as the texture and edge which is contained in the blurred image. However, it is a computational burden to train a new dictionary for image deblurring which requires the whole image (or most parts) as input; training the dictionary on only a few patches would result in over-fitting. To address the problem, we instead propose an online adaption strategy to transfer the global learned dictionary to a specific image. In our deblurring algorithm, the sparse coefficients, latent image, blur kernel and the dictionary are updated alternatively. And in every step, the global learned dictionary is updated in an online form via sampling only a few training patches from the target noisy image. Since our adaptive dictionary exploits the specific information, our deblurring algorithm shows superior performance over other state-of-the-art algorithms.

Image Deblur in Gradient Domain

This paper proposes a new method for natural-image deblur based on a single blurred image. The natural image prior, a sparse gradient distribution, is enforced using a gradient histogram remapping method in the proposed deblur algorithm. The proposed objective function for blind deconvolution is solved by an alternating minimization method. The point spread function and the unblurred image are updated alternately. The proposed method is able to produce high-quality deblurred results with low computational costs. Both synthetic and real blurred images are tested in the experiments. Encouraging experimental results show that the newly proposed method could effectively restore images blurred by complex motion

Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation

This paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. The proposed method estimates a point-spread-function (PSF) of motion blur by accumulating reweighted projections of the trajectory. A motion blurred image is then adaptively restored using the estimated PSF and spatially varying activity map to reduce both restoration artifacts and noise amplification. Experimental results demonstrate that the proposed method outperforms existing PSF estimation-based motion deconvolution methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed in various imaging devices because of its efficient implementation without an iterative computational structure

Rotational motion deblurring of a rigid object from a single image

Most previous motion deblurring methods restore the degraded image assuming a shift-invariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2-D rigid body movement and propose a practical framework to deblur rotational motions from a single image. Our main observation is that the transparency cue of a blurred object, which represents the motion blur formation from an imaging perspective, provides sufficient information in determining the object movements. Comparatively, single image motion deblurring using pixel color/gradient information has large uncertainties in motion representation and computation. Our results are produced by minimizing a new energy function combining rotation, possible translations, and the transparency map using an iterative optimizing process. The effectiveness of our method is demonstrated using challenging image examples. anteed since the convolution with a blur kernel is noninvertible. To tackle this problem, additional image priors, such as the global gradient distribution from clear images [7], are proposed. Some approaches use multiple images or additional visual cues [2, 20] to constrain the kernel estimation. (a) (b