Edgar Salazar

Research Professor
Universidad Privada Boliviana
Bolivia

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Keynote Talk

Solving the Inverse Problem in Compressive Compton Backscattering: A Generative Diffusion Null Space Approach

Abstract

Rapid and high-fidelity body scanning is a critical requirement for security at high-throughput checkpoints. Due to their advantages in resolution, Compton scattering-based scanners are preferred when targets are continuously moving and non-static snapshots must be captured.

Recently, the sensing paradigm in Compton scanning has been revolutionized using computational imaging, through binary coded illumination. This gave rise to the Compressive X-ray Compton Backscattering Imager (CXBI), which accelerates acquisition by recovering high-quality images from a limited set of low-dimensional projections.

In this talk, we present recent advancements in CXBI, focusing on state-of-the-art Generative AI. We introduce the Invertible Diffusion Model in CXBI, which reformulates the Denoising Diffusion Null-Space Model (DDNM) as a trainable, unrolled architecture for compressed sensing, and benefits from rich hierarchical representations previously learned from extensive image datasets.

We further discuss the joint optimization of the sensing layer and diffusion-like reconstruction, and the impact of Poisson-like noise statistics on algorithmic performance. Furthermore, we share ongoing experimental results and discuss the future perspectives and challenges of deploying generative frameworks in real-world security environments.

Andrés G. Marrugo, Ph.D.

Universidad Tecnológica de Bolívar
Colombia

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Keynote Talk

Advances in Calibration for 3D Active Sensing: From Accurate 3D Reconstruction to Digital Twins

Abstract

Calibration is essential to the accuracy and reliability of 3D active sensing systems. This talk presents recent advances in camera–projector calibration, geometric modeling, and large-scale structured-light sensing. It also discusses how improved calibration supports accurate 3D reconstruction and the development of trustworthy digital twins.