Download 3-D Shape Estimation and Image Restoration: Exploiting by Paolo Favaro PDF

By Paolo Favaro

Within the components of picture processing and machine imaginative and prescient, there's a specific want for software program which can, given an unfocused or motion-blurred photograph, infer the 3-dimensional form of a scene. This publication describes the analytical procedures that cross into designing such software program, delineates the choices open to programmers, and provides unique algorithms. Written for readers with pursuits in picture processing and machine imaginative and prescient and with backgrounds in engineering, technological know-how or arithmetic, this hugely sensible text/reference is out there to complex scholars or people with a level that incorporates easy linear algebra and calculus classes.

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

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This is due to the fact that the imaging model we use is only a coarse approximation of the real image formation process. 3). The measured image J is a realization of a complex process, part of which is modeled probabilistically using a probability density pn . The most common choices are the Gaussian and the Poisson distributions. These can be justified in an axiomatic way [Csisz´ar, 1991] by defining a set of desirable properties that a discrepancy measure should satisfy. Csisz´ar concludes that, when the quantities involved are constrained to be positive, the only choice of criterion consistent with his axioms is the information-divergence, which we will define shortly.

If a radiance r : R2 → R is harmonic, then, for any kernel h : R2 → R that is rotationally symmetric, we have h(y − x)r(x)dx = r(y) h(x)dx. 11) Proof. 2. By the energy conservation principle, hvs (y, x) dy = 1; when we are imaging an equifocal plane, the kernel hvs is indeed rotationally symmetric and shiftinvariant. The mean-value property tells us that, if r is harmonic, its image under any focus setting is the same, hence no information on shape can be obtained from defocused images. 6. 12) is independent of s, so that we can conclude that harmonic radiances do not allow distinguishing scenes with different shape.

4). 1). In addition to the general ambiguities that we describe in this chapter, even in the idealized noise-free case there is no unique solution of the problem of reconstructing shape and radiance from defocused images because the unknowns are infinite-dimensional, whereas the images are digitized and quantized to finite dimensions. In fact, shape from defocus is a particular case of blind deconvolution, that is an ill-posed problem, as we discuss in Appendix F. 5. 38 3. Some analysis: When can 3-D shape be reconstructed from blurred images?

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