Publication year: 2007
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Images contain information about the spatial properties of the scene they depict. When coupled with suitable assumptions, images can be used to infer three-dimensional information. This useful volume concentrates on motion blur and defocus, which can be exploited to infer the 3-D structure of a scene—as well as its radiance properties—and which in turn can be used to generate novel images with better quality. 3-D Shape Estimation and Image Restoration presents a coherent framework for the analysis and design of algorithms to estimate 3-D shape from defocused and motion blurred images, and to eliminate defocus and motion blur to yield "restored" images. It provides a collection of algorithms that are optimal with respect to the chosen model and estimation criterion.
Subject: Computer Science, 3D, 3D Modeling, 3D Reconstruction, 3D Shape Estimation, Computer Graphics, Computer Vision, Confocal Imaging, Deblurring, Defocus, Image Restoration, Motion Blur, Optics, image processing