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Image Processing: Tensor Transform and Discrete Tomography by Artyom M. Grigoryan

Posted On March 23, 2017 at 10:04 pm by / Comments Off on Image Processing: Tensor Transform and Discrete Tomography by Artyom M. Grigoryan

By Artyom M. Grigoryan

Concentrating on mathematical equipment in desktop tomography, photograph Processing: Tensor rework and Discrete Tomography with MATLAB® introduces novel techniques to aid in fixing the matter of snapshot reconstruction at the Cartesian lattice. particularly, it discusses tools of photo processing alongside parallel rays to extra speedy and appropriately reconstruct pictures from a finite variety of projections, thereby heading off overradiation of the physique in the course of a computed tomography (CT) scan.

The ebook provides numerous new rules, suggestions, and strategies, lots of that have no longer been released in different places. New recommendations contain tools of shifting the geometry of rays from the airplane to the Cartesian lattice, the purpose map of projections, the particle and its box functionality, and the statistical version of averaging. The authors offer various examples, MATLAB®-based courses, end-of-chapter difficulties, and experimental result of implementation.

The major procedure for photograph reconstruction proposed by means of the authors differs from present equipment of back-projection, iterative reconstruction, and Fourier and Radon filtering. during this e-book, the authors clarify how one can technique every one projection by way of a method of linear equations, or linear convolutions, to calculate the corresponding a part of the 2-D tensor or paired remodel of the discrete snapshot. They then describe tips on how to calculate the inverse rework to acquire the reconstruction. The proposed types for picture reconstruction from projections are uncomplicated and lead to extra exact reconstructions.

Introducing a brand new thought and techniques of picture reconstruction, this publication presents a pretty good grounding for these drawn to additional learn and in acquiring new effects. It encourages readers to improve potent functions of those equipment in CT.

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Extra info for Image Processing: Tensor Transform and Discrete Tomography with MATLAB

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We now consider the same sets on the discrete torus. 4 shows the locus of two spirals S3 and S7 on the net, for (p, s) = (1, 1). 1). 4 (See color insert) The net with knots of the grid 32 × 32 in the 3-D space with locus of two spirals S1,1,3 and S1,1,7 . points. These points lie on the spiral that passes through the initial point (0, 0) of the net and make an angle π/2 with the spirals St . Each image-signal is the set of discrete integrals along the family Lp1 ,p2 ,t of parallel lines. Therefore, the processing of the image-signal fT yields the change in the Fourier transform at frequency-points of the corresponding cyclic group Tp1 ,p2 .

The star in these figures denotes the value of F0 . 17 DFT components F1 and F2 . 18 DFT components F3 and F4 . 19 DFT components F66 and F67. 20 DFT components F115 and F120 . located on the real line and then are rotated, step-by-step, N 2 times in the plane R2 . The process of rotation of all points is more complicated when comparing with the N -point DFT. , f1,1 → W 1 f1,1 → W 2 f1,1 → W 3 f1,1 → ... , F0,N−1 , and also rotated as f1,1 → W 2 f1,1 → W 3 f1,1 → W 4 f1,1 → ... , FN−1,1, and so on.

At the last step, the four-point DFT of the splitting-signal fT1,2 = {7, 9, 8, 4} is calculated. It equals (E0 , E1, E2 , E3 ) = (28, −1 − 5j, 2, −1 + 5j) and defines the 2-D DFT of f at the frequency-points (0, 0), (1, 2), (2, 0), and (3, 2). We fill the 2-D DFT with the values of E1 and E2 , 2 F0 6 D1 6 4 D2 D3 F1 A1 B1 C1 F2 E1 A2 E3 3 2 3 2 28 F3 F0,0 1−j −6 1+j 6 7 6 C3 7 7 = 6 −1 + 5j 4 + 2j −1 − 5j −2 − 2j 7 = 6 F1,0 B3 5 4 2 −3 + j −4 −3 − j 5 4 F2,0 A3 F3,0 −1 − 5j −2 + 2j −1 + 5j 4 − 2j F0,1 F1,1 F2,1 F3,1 F0,2 F1,2 F2,2 F3,2 3 F0,3 F1,3 7 7.

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