By Paolo Favaro

ISBN-10: 1846281768

ISBN-13: 9781846281761

ISBN-10: 1846286883

ISBN-13: 9781846286889

In the parts of photograph processing and desktop imaginative and prescient, there's a specific desire for software program which can, given an unfocused or motion-blurred photograph, infer the third-dimensional form of a scene. This booklet describes the analytical techniques that pass into designing such software program, delineates the choices open to programmers, and provides unique algorithms. Written for readers with pursuits in photograph processing and computing device imaginative and prescient and with backgrounds in engineering, technology or arithmetic, this hugely functional text/reference is available to complex scholars or people with a level that comes with easy linear algebra and calculus courses.

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Our main concern from now on is to use this equation to try to infer shape and radiance given a number of images taken with different settings. Before we venture into the design of algorithms to infer shape and radiance from blurred images, however, we need to ask ourselves whether this problem can be solved at all. It is easy to concoct scenes that cannot be reconstructed based solely on defocus information. Take, for instance, a white cube illuminated uniformly on a white background. We will never be able to tell it apart from a white sphere on a white background, no matter how many defocused images we take.

This is equivalent to requiring that our optical system be lossless, so that all the energy emitted by a point in the scene is transferred to the image plane and our camera does not absorb or dissipate energy. 28 2. Basic models of image formation We now resume our physical analogy and consider the case of a flat (equifocal) scene. 1 Equifocal imaging as isotropic diffusion Suppose our scene is a flat metal plate, with a heat distribution corresponding to its radiance; that is, the temperature at a point represents the radiant energy.

Occasionally we may be interested in inferring the radiance as well, in which case we talk about deblurring, or image restoration . As mentioned in Chapter 2, the kernel h is a function hvs : Ω × R2 → [0, ∞) where Ω ⊂ R2 is a bounded subset of R2 . For instance, we may consider kernels belonging to the Gaussian family hvs (y, x) = x−y 2 /(2σ 2 ) . that the variables x and y are two-dimensional vectors of real numbers. some instances, in order to infer the surface s we may have to retrieve r as well.

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3-D shape estimation and image restoration : exploiting defocus and motion blur by Paolo Favaro


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