Research Article Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation Eunsung Lee, Eunjung Chae, Hejin Cheong, and Joonki Paik Department of Image, Chung-Ang University, Seoul 156-756, Republic of Korea Correspondence should be addressed to Joonki Paik; paikj@cau.ac.kr Received 29 July 2014; Accepted 10 October 2014; Published 4 November 2014 Academic Editor: Yung-Kuan Chan Copyright © 2014 Eunsung Lee et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. his paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. he 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 ampliication. 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. he proposed algorithm can be employed in various imaging devices because of its eicient implementation without an iterative computational structure. 1. Introduction Restoration of motion blurred images is a fundamental prob- lem of image processing especially under a poor illumination condition, where a long exposure creates unwanted motion blur. A number of blind image deconvolution methods have been proposed to remove motion blur. In this context practical blind image deconvolution can be categorized into three main varieties: single image-based, multiple image- based, and hardware-aided approaches. Single image-based blind deconvolution estimates the blur kernel in the form of a point-spread-function (PSF) based on a simple parametric model using a single input image [1, 2]. However, a simple parametric curve cannot successfully represent the motion PSF made by various types of real camera motions. Fergus et al. proposed a general motion PSF estimation method which uses a sophisticated variational Bayesian method based on the natural image prior [3], which was followed up by related research in [4– 8]. Although these methods provide a generalized camera motion model, a manual process of tuning parameters and high computational load are their disadvantages. he multiple image-based blind deconvolution removes motion blur by appropriately combining long- and short- exposure images under the assumption that both images are captured from the same scene at the same time [9–11]. If the simultaneous acquisition assumption does not hold, the multiple image-based approach fails to remove motion blur. he hardware-aided approach uses additional optical devices or electronic systems to overcome the limitations of the multiple image-based approach [12–16]. In spite of acquiring more accurate, robust data to estimate the motion PSF, the hardware-aided method needs a complicated optical system such as a coded-exposure or an embedded inertial sensor. An eicient implementation method of a built-in inertial sensor was introduced by ˇ Sindel´ aˇ r and ˇ Sroubek for mobile imaging devices [16]. But the performance of motion deblurring is not good enough because of the sensor noise and the use of a simple restoration ilter. For fast motion deblurring, both PSF estimation and the corresponding image restoration should be fast and accurate. In this paper, an adaptive image deblurring method is pre- sented by generating the motion trajectory in the probabilis- tic manner and performing image restoration based on the local statistics to solve common issues in the deconvolution process. he contribution of the proposed research is twofold: (i) a novel motion PSF estimation method is proposed by minimizing the motion trajectory error based on a priori probability distribution, and (ii) a noniterative adaptive image restoration algorithm is proposed based on the local Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 649272, 7 pages http://dx.doi.org/10.1155/2014/649272