Can Evren Yarman

About

Can Evren Yarman is a researcher whose work bridges harmonic analysis, stochastic signal processing, and inverse problems, with a particular focus on the Radon transform—a cornerstone of medical imaging, synthetic aperture radar, and radio astronomy. His key contributions center on a fundamentally novel formulation of the Radon transform as a convolution integral over the Euclidean motion group (SE(2)). This geometric insight allows him to recast the inversion problem as a deconvolution, enabling the application of Wiener filtering techniques for optimal, minimum mean square error (MMSE) reconstruction. His most cited paper, "Radon transform inversion via Wiener filtering over the Euclidean motion group" (2004, 11 citations), and its precursor (2003, 3 citations) establish this elegant framework, offering a stochastic alternative to classical analytic inversion methods. While his citation counts reflect a specialized, theoretical niche, Yarman’s work is notable for introducing group-theoretic and stochastic principles to a classic problem, potentially offering robustness in noisy or limited-data scenarios. His approach represents a creative synthesis of abstract mathematics and practical signal processing, marking him as an innovative thinker in the field of computational imaging and inverse theory.

Research Focus

Key Achievements

Top Papers

1. 1
   Radon transform inversion via Wiener filtering over the Euclidean motion group

   11 citations · 2004
2. 2
   A Wiener Filtering Approach over the Euclidean Motion Group for Radon Transform Inversion

   3 citations · 2003

Key Collaborators

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