scholarly journals Central and Periodic Multi-Scale Discrete Radon Transforms

2021 ◽  
Vol 11 (22) ◽  
pp. 10606
Author(s):  
Óscar Gómez-Cárdenes ◽  
José G. Marichal-Hernández ◽  
Jonas Phillip Lüke ◽  
José M. Rodríguez-Ramos

The multi-scale discrete Radon transform (DRT) calculates, with linearithmic complexity, the summation of pixels, through a set of discrete lines, covering all possible slopes and intercepts in an image, exclusively with integer arithmetic operations. An inversion algorithm exists and is exact and fast, in spite of being iterative. In this work, the DRT forward and backward pair is evolved to propose two faster algorithms: central DRT, which computes only the central portion of intercepts; and periodic DRT, which computes the line integrals on the periodic extension of the input. Both have an output of size N×4N, instead of 3N×4N, as in the original algorithm. Periodic DRT is proven to have a fast inversion, whereas central DRT does not. An interesting application of periodic DRT is its use as building a block of discrete curvelet transform. Central DRT can provide almost a 2× speedup over conventional DRT, probably becoming the faster Radon transform algorithm available, at the cost of ignoring 15% of the summations in the corners.

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. V1-V11 ◽  
Author(s):  
Amr Ibrahim ◽  
Mauricio D. Sacchi

We adopted the robust Radon transform to eliminate erratic incoherent noise that arises in common receiver gathers when simultaneous source data are acquired. The proposed robust Radon transform was posed as an inverse problem using an [Formula: see text] misfit that is not sensitive to erratic noise. The latter permitted us to design Radon algorithms that are capable of eliminating incoherent noise in common receiver gathers. We also compared nonrobust and robust Radon transforms that are implemented via a quadratic ([Formula: see text]) or a sparse ([Formula: see text]) penalty term in the cost function. The results demonstrated the importance of incorporating a robust misfit functional in the Radon transform to cope with simultaneous source interferences. Synthetic and real data examples proved that the robust Radon transform produces more accurate data estimates than least-squares and sparse Radon transforms.


1997 ◽  
Vol 75 (1) ◽  
pp. 39-61 ◽  
Author(s):  
Peter Fishburn ◽  
Peter Schwander ◽  
Larry Shepp ◽  
Robert J. Vanderbei

2014 ◽  
Vol 9 (S1) ◽  
pp. 145-154
Author(s):  
Ines ELouedi ◽  
Régis Fournier ◽  
Amine Naït-Ali ◽  
Atef Hamouda

2021 ◽  
Author(s):  
Li Lu Yang ◽  
Li Liang Fu ◽  
Song Yu Hang ◽  
Quan Hong Yan ◽  
Zhao Mi Yang ◽  
...  

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