# SABER: A Systems Approach to Blur Estimation and Reduction in X-ray Imaging

## TL;DR

SABER introduces a systems-based method for modeling, estimating, and reducing blur in X-ray images by combining multiple PSFs and applying deblurring algorithms, improving image clarity and contrast.

## Contribution

The paper presents a novel systems approach to model and estimate the combined source and detector PSFs in X-ray imaging, enabling effective blur reduction.

## Key findings

- Accurate estimation of source and detector PSFs from multiple radiographs.
- Effective blur reduction using estimated PSFs through deblurring algorithms.
- Improved image sharpness and contrast in X-ray radiographs.

## Abstract

Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast. The effective blur in a radiograph is the combined effect of blur from multiple sources such as the detector panel, X-ray source spot, and system motion. In this paper, we use a systems approach to model the point spread function (PSF) of the effective radiographic blur as the convolution of multiple PSFs, where each PSF models one of the various sources of blur. In particular, we model the combined contribution of X-ray source and detector blurs while assuming negligible contribution from other forms of blur. Then, we present a numerical optimization algorithm for estimating the source and detector PSFs from multiple radiographs acquired at different X-ray source to object (SOD) and object to detector distances (ODD). Finally, we computationally reduce blur in radiographs using deblurring algorithms that use the estimated PSFs from the previous step. Our approach to estimate and reduce blur is called SABER, which is an acronym for systems approach to blur estimation and reduction.