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

By Paolo Favaro

Within the parts of snapshot processing and computing device 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 publication describes the analytical tactics that pass into designing such software program, delineates the choices open to programmers, and provides unique algorithms. Written for readers with pursuits in snapshot processing and desktop 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 uncomplicated linear algebra and calculus classes.

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Y , i = j. This is because the “background” scene can be considered to be an object itself. In this case, the image model becomes I(y) = √ 2 1 ˜2 e−t /(2ΔT ) r(y + νj (y, t))dt 2πΔT˜ ∀ y ∈ Ωj . 40) In order to complete the model we need to determine the velocities νj , which are a function of the shape and motion of objects in the scene. To do so consider again, for simplicity, the case of a scene made of a single object with depth map s. 41) where × denotes the cross-product 10 of two vectors in R3 .

14) identifies an infinite set of components that, when combined, give rise to the radiance r. Typically we can sort αk in decreasing order, and truncate beyond a certain index. When the sum is actually finite, we can give a name to the maximum index in the sum. 7. ρ , with αk = 0, k = 0, . . , ρ, such that ρ αk θk (x). ∞ , we say that the distribution r is band-limited if the corresponding degree of resolution ρ < ∞. Similarly, we can define a degree of resolution for the kernel h. 8. Let hvs ∈ L2 (R2 ×R2 ).

30) with initial conditions u(y, 0) = δ(x − y), and δ(·) is Dirac’s delta. The function G satisfies the equation u(x, ˙ t) = c u(x, t) u(x, 0) = r(x) u(y, t) = G(y, x, t)u(x, 0)dx = G(y, x, t)r(x)dx. 30). This explains why we so cherish Gaussians as point-spread functions, beyond the discussion leading to this section. Gaussians establish the equivalence between 5 Notational mea culpa: we use u for the solution of the isotropic heat equation as is customary in the PDE literature. Alas, we have already used u to describe the geometry of the lens.

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