arXiv subjects

Olli Koskela

Publications and source records attributed to Olli Koskela.

3 recordsLinked to original sources

arXiv2026

Torus computed tomography for experimental data

We implement the torus-based X-ray tomography method introduced by Ilmavirta, Koskela, and Railo in "Torus computed tomography", SIAM J. Appl. Math., 80(4):1947--1976, 2020, for experimental X-ray tomographic data. The numerical implementation is extended to accommodate fan-beam measurements by converting the data to a parallel-beam format and mapping the projection angles to the closed-geodesic directions on the torus. In addition, we consider two extensions of the original framework: the Star TCT method which extends the frequency coverage of the reconstruction, and a numerical implementation of torus backprojection developed by Railo in "Fourier analysis of periodic Radon transforms", J. Fourier Anal. Appl., 26(4):64, 2020, for which we also derive a corresponding regularized formulation. We demonstrate the methods on experimental X-ray data of a walnut and compare them with filtered backprojection. We also introduce a pointwise positivity constraint as a post-processing step, which substantially improves the reconstruction accuracy. The simulated data experiments are revisited using an updated implementation. The results indicate that the proposed extensions improve reconstruction quality and support the applicability of torus-based reconstruction methods to experimental data.

math.NA↗

arXiv2025

Per-channel autoregressive linear prediction padding in tiled CNN processing of 2D spatial data

We present linear prediction as a differentiable padding method. For each channel, a stochastic autoregressive linear model is fitted to the padding input by minimizing its noise terms in the least-squares sense. The padding is formed from the expected values of the autoregressive model given the known pixels. We trained the convolutional RVSR super-resolution model from scratch on satellite image data, using different padding methods. Linear prediction padding slightly reduced the mean square super-resolution error compared to zero and replication padding, with a moderate increase in time cost. Linear prediction padding better approximated satellite image data and RVSR feature map data. With zero padding, RVSR appeared to use more of its capacity to compensate for the high approximation error. Cropping the network output by a few pixels reduced the super-resolution error and the effect of the choice of padding method on the error, favoring output cropping with the faster replication and zero padding methods, for the studied workload.

cs.LG↗

arXiv2020

Torus computed tomography

We present a new computed tomography (CT) method for inverting the Radon transform in 2D. The idea relies on the geometry of the flat torus, hence we call the new method Torus CT. We prove new inversion formulas for integrable functions, solve a minimization problem associated to Tikhonov regularization in Sobolev spaces and prove that the solution operator provides an admissible regularization strategy with a quantitative stability estimate. This regularization is a simple post-processing low-pass filter for the Fourier series of a phantom. We also study the adjoint and the normal operator of the X-ray transform on the flat torus. The X-ray transform is unitary on the flat torus. We have implemented the Torus CT method using Matlab and tested it with simulated data with promising results. The inversion method is meshless in the sense that it gives out a closed form function that can be evaluated at any point of interest.

math.FA↗