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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 sceneas 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.

Topics and Features include:
*Comprehensive introduction to guide readers through the different areas of the topic
*Basic models of image formation
*Discussion of least-squares shape from defocus
*Unifying defocus and motion blur
*Handling multiple moving objects
*Dealing with occlusions
*Appendices supply the necessary background in optimization and regularization

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