Get 3-D shape estimation and image restoration : exploiting PDF

By Paolo Favaro

ISBN-10: 1846281768

ISBN-13: 9781846281761

ISBN-10: 1846286883

ISBN-13: 9781846286889

In the components of picture processing and desktop imaginative and prescient, there's a specific desire for software program which could, given an unfocused or motion-blurred snapshot, infer the 3-dimensional form of a scene. This booklet describes the analytical approaches that cross into designing such software program, delineates the choices open to programmers, and offers unique algorithms. Written for readers with pursuits in photo processing and laptop imaginative and prescient and with backgrounds in engineering, technology or arithmetic, this hugely sensible text/reference is offered to complicated scholars or people with a level that comes with uncomplicated linear algebra and calculus courses.

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Extra resources for 3-D shape estimation and image restoration : exploiting defocus and motion blur

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3. The role of radiance 41 possible to change r by projecting structured light patterns onto the scene, this is not possible in general. The definition of weak observability leaves us the freedom to choose the radiance r, possibly as a function of s, to distinguish s from s . However, one may wonder if a radiance r can be found that allows distinguishing any s. We address this issue in the next section. 3 The role of radiance Let I(s|r) denote the set of surfaces that cannot be distinguished from s for a given radiance r: I(s|r) = {˜ s | Is˜r (y, v) = Isr (y, v) ∀ y ∈ Ω, v ∈ V}.

Basic models of image formation task is difficult due to the loss of information caused by the image formation process. Hence, one may wonder whether the inverse problem can be solved at all. In the next chapter we address precisely this question. Before the reader can successfully reconstruct 3-D structure and radiance using the algorithms described in the next few chapters, it is necessary to precalibrate the images in order to eliminate magnification artifacts and other errors in the registration of different blurred images.

10. The practical value of the last proposition is to state that, the more “irregular” the radiance, the more resolving power it has. When we can project structured light on the scene, the proposition establishes that “high-frequency” patterns should be used in conjunction with shape from defocus. An “infinitely irregular” pattern should therefore be universally exciting, as conjectured in the previous section. This is why in the experiments in the next chapters we always use highly textured scenes.

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


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