By Paolo Favaro

ISBN-10: 0387279431

ISBN-13: 9780387279435

ISBN-10: 1846281768

ISBN-13: 9781846281761

ISBN-10: 1846286883

ISBN-13: 9781846286889

Within the components of photo processing and laptop imaginative and prescient, there's a specific want for software program that could, given an unfocused or motion-blurred picture, infer the third-dimensional form of a scene. This booklet describes the analytical techniques that cross into designing such software program, delineates the choices open to programmers, and provides unique algorithms. Written for readers with pursuits in snapshot processing and machine imaginative and prescient and with backgrounds in engineering, technological know-how or arithmetic, this hugely sensible text/reference is offered to complex scholars or people with a level that comes with easy linear algebra and calculus classes.

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Extra info for 3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur

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Objects in the scene can emit energy of their own, or transmit energy from third sources in ways that depend on their material. Rough wood or opaque matte surfaces, for instance, reflect light homogeneously in all directions (Lambertian materials), whereas polished stone or metal have a preferred direction. Even more complex is the case of translucent materials such as alabaster or skin, where light is partially absorbed and reflected after subsurface scattering. A more detailed description of materials and their reflective properties can be found in Appendix A.

Nevertheless, analysis based on an idealized model can shed light on the design of algorithms operating on real data. Under the above conditions we can state the following result. 2. Any smooth surface s is weakly observable. Proof. 1. 2 shows that it is possible, in principle, to distinguish the shape of a surface from that of any other surface by looking at images under different camera settings v and radiances r. This, however, requires the active usage of the control v and of the radiance r.

Therefore, I : Ω × V → [0, ∞); (y, v) → I v (y) although occasionally we use the notation I(y, v) or I(y; v). To summarize, we have I(y, v) = hv (y, x)r(x)dx. 4), or the pointspread function (PSF) of the optics. 1, the radius b is constant and, therefore, the function hv is shiftinvariant; that is, it depends only on the difference y−x. With an abuse of notation we write . hv (y − x) = hv (y, x). 4) is derived from a more complex quantity called radiance. A full derivation can be found in Appendix A.

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3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur by Paolo Favaro


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