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A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields.
The computer vision community is witnessing a major resurgence in the area of motion deblurring spurred by the emerging ubiquity of portable imaging devices. Rapid strides are being made in handling motion blur both algorithmically and through tailor-made hardware-assisted technologies. The main goal of this book is to ensure a timely dissemination of recent findings in this very active research area. Given the flurry of activity in the last few years in tackling uniform as well as non-uniform motion blur resulting from incidental shake in hand-held consumer cameras as well as object motion, we felt that a compilation of recent and concerted efforts for restoring images degraded by motion blur was well overdue. Since no single compendium of the kind envisaged here exists, we believe that this is an opportune time for publishing a comprehensive collection of contributed chapters by leading researchers providing in-depth coverage of recently developed methodologies with excellent supporting experiments, encompassing both algorithms and architectures.
As is well known, the main cause of motion blur is the result of averaging of intensities due to relative motion between a camera and a scene during exposure time. Motion blur is normally considered a nuisance although one must not overlook the fact that some works have used blur for creating aesthetic appeal or exploited it as a valuable cue in depth recovery and image forensics. Early works were non-blind in the sense that the motion blur kernel (i.e. the point spread function (PSF)) was assumed to be of a simple form, such as those arising from uniform camera motion, and efforts were primarily directed at designing a stable estimate for the original image.
The need to recognize motion-blurred faces is vital for a wide variety of security applications ranging from maritime surveillance to road traffic policing. While much of the theory in the analysis of motion-blurred images focuses on restoration of the blurred image, we argue that this is an unnecessary and expensive step for face recognition. Instead, we adopt a direct approach based on the set-theoretic characterization of the space of motion-blurred images of a single sharp image. This set lacks the nice property of convexity that was exploited in a recent paper to achieve competitive results in real-world datasets (Vageeswaran, Mitra & Chellappa 2013). Keeping this non-convexity in mind, we propose a bank of classifiers (BoC) approach for directly recognizing motion-blurred face images. We divide the parameter space of motion blur into many different bins in such a way that the set of blurred images within each bin is a convex set. In each such bin, we learn support vector machine (SVM) classifiers that separate the convex sets associated with each person in the gallery database. Our experiments on synthetic and real datasets provide compelling evidence that this approach is a viable solution for recognition of motion-blurred face images.
A system that can recognize motion-blurred faces can be of vital use in a wide variety of security applications, ranging from maritime surveillance to road traffic policing. Figure 12.1 shows two possible maritime surveillance scenarios: shore-to-ship (the camera is mounted on-shore and the subjects are in the ship), and ship-to-shore (the camera is on the ship and the subjects are moving on-shore).
Multicamera networks are becoming increasingly common in surveillance applications given their ability to provide persistent sensing over a large area. Opportunistic sensing and nonintrusive acquisition of biometrics, which are useful in many applications, come into play. However, opportunistic sensing invariably comes with a price, namely, a wide range of potential nuisance factors that alter and degrade the biometric signatures of interest. Typical nuisance factors include pose, illumination, defocus blur, motion blur, occlusion, and weather effects. Having multiple views of a person is critical for mitigating some of these degradations.
In particular, having multiple viewpoints helps build more robust signatures because the system has access to more information. For face recognition, having multiple views increases the chances of the person being in a favorable frontal pose. However, to use the multiview information reliably, we need to estimate the pose of the person's head. This could be done explicitly by computing the actual pose of the person to a reasonable approximation, or implicitly by using a view selection algorithm. Solving for the pose of a person's head presents a difficult problem, especially when images have poor resolution and the calibration of cameras (both external and internal) is not sufficiently precise to allow robust multiview fusion. This holds especially true in surveillance applications when the subjects under surveillance often appear in the far- field of the camera.
Recent advances in computer vision address the problem of registration of multiple images or entire video sequences. Such registration methods have a wide variety of application in constructing mosaics, video summarization, site modeling and as preprocessing for tasks such as object tracking and recognition. In this chapter we present a variety of registration techniques that utilize image features such as points and contours. Computational issues such as robustness to data outliers and recent developments in accurate feature extraction are discussed. A correspondenceless method that works on multimodal images is outlined. We also present approaches that efficiently utilize the information redundancy in a sequence of images to solve the problem of image registration. All of these methods are illustrated with appropriate examples.
The underlying geometry of image formation has been well studied over the recent years in the discipline of computer vision (Hartley and Zisserman, 2004). This understanding of the image geometry has been accompanied by increasingly sophisticated computational models that can be solved on modern hardware. Many methods and ideas developed for solving various aspects of the motion estimation problem in computer vision are applicable to problems relating to image registration. In particular, image features like points, edges, and contours have been used in a range of applications like the construction of mosaics from video sequences, shape estimation, object tracking and recognition, etc. In this chapter, we describe a variety of methods dedicated to utilizing image features for solving the problem of registration.