Generalized Backpropagation Algorithms for Diffraction Tomography
Pavel Roy Paladhi, Ashoke Sinha, Amin Tayebi, Lalita Udpa

TL;DR
This paper develops and compares generalized weighting filters for diffraction tomography, enabling accurate image reconstruction with less than the standard 270° angular coverage, even under noisy conditions.
Contribution
It introduces a framework for generating various weighting filters that improve image reconstruction at reduced angular coverages in diffraction tomography.
Findings
Optimal filters achieve near full-coverage quality at 200°.
Performance degrades gracefully with noise, maintaining high-quality reconstructions.
Filters tailored for sub-minimal angles outperform traditional methods.
Abstract
Filtered backpropagation (FBPP) is a well-known technique used for Diffraction Tomography (DT). For accurate reconstruction of a complex image using FBPP, full angular coverage is necessary. However, it has been shown that using some inherent redundancies in projection data in a tomographic setup, accurate reconstruction is still possible with coverage which is called the minimal-scan angle range. This can be done by applying weighing functions (or filters) on projection data of the object to eliminate the redundancies and accurately reconstruct the image from this lower angular coverage. This paper demonstrates procedures to generate many general classes of these weighing filters. These are all equivalent at coverage but would perform differently at lower angular coverages and under presence of noise. This paper does a comparative analysis of…
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Taxonomy
TopicsGeophysical Methods and Applications · Microwave Imaging and Scattering Analysis · Seismic Imaging and Inversion Techniques
