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8 - Richardson–Lucy deblurring for scenes under a projective motion path

Published online by Cambridge University Press:  05 June 2014

By
Yu-Wing Tai  and
Michael S. Brown
Edited by
A. N. Rajagopalan  and
Rama Chellappa
Yu-Wing Tai
Affiliation:
Korea Advanced Institute of Science and Technology
Michael S. Brown
Affiliation:
National University of Singapore
A. N. Rajagopalan
Affiliation:
Indian Institute of Technology, Madras
Rama Chellappa
Affiliation:
University of Maryland, College Park
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Summary

Introduction

Motion blur from camera egomotion is an artifact in photography caused by the relative motion between the camera and an imaged scene during exposure. Assuming a static and distant scene, and ignoring the effects of defocus and lens aberration, each point in the blurred image can be described as the convolution of the un-blurred image by a point spread function (PSF) that describes the relative motion trajectory at that point's position. The aim of image deblurring is to reverse this convolution process to recover the clear image of the scene from the captured blurry image as shown in Figure 8.1.

A common assumption in existing motion deblurring algorithms is that the motion PSF is spatially invariant. This implies that all pixels are convolved with the same motion blur kernel. However, as discussed by Levin, Weiss, Durand & Freeman (2009) the global PSF assumption is often invalid. In their experiments, images taken with camera shake exhibited notable amounts of rotation that attributed to spatially-varying motion blur within the image. Figure 8.2 shows a photograph that illustrates this effect. As a result, Levin et al. (2009) advocated the need for a better motion blur model as well as image priors to help regularize the solution space when performing deblurring. This chapter addresses the former issue by introducing a new and compact motion blur model that is able to describe spatially-varying motion blur caused by a camera undergoing egomotion.

Type
Chapter
Information
Motion Deblurring
Algorithms and Systems
 , pp. 161 - 183
Publisher: Cambridge University Press
Print publication year: 2014

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