By Paolo Favaro
Within the components of photograph processing and computing device imaginative and prescient, there's a specific want for software program which may, given an unfocused or motion-blurred picture, infer the third-dimensional form of a scene. This ebook describes the analytical techniques 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 desktop 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 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
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. The degree of resolution with which two surfaces can be distinguished also depends upon the optics of the imaging system. In our analysis this is taken into account by the degree of complexity of the radiance, that can itself be limited, or it can be limited by the optics of the imaging system.
Therefore, again in principle, it is possible to reconstruct the shape of a scene uniquely. Consider two scenes with surfaces s and s , respectively. 40 3. Some analysis: When can 3-D shape be reconstructed from blurred images? 1. We say that a surface s is weakly indistinguishable from a surface s if, for all possible radiances r, there exists at least a radiance r such that Isr (y, v) = Isr (y, v) ∀y∈Ω ∀ v ∈ V. 6) Two surfaces are weakly distinguishable if they are not weakly indistinguishable.
So, clearly, whether we can reconstruct 3-D shape depends on the radiance of the scene, which we do not know. Under what conditions, then, can we reconstruct shape and radiance? In this chapter we address this question and its many facets. We organize our analysis around the role of radiance. 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.