A Microwave Imaging and Enhancement Technique from Noisy Synthetic Data

TL;DR

This paper introduces an inverse iterative algorithm for microwave imaging using synthetic noisy data, employing constrained optimization and image enhancement to improve reconstruction quality.

Contribution

It presents a novel microwave imaging method that combines iterative constrained optimization with noise handling and image enhancement techniques.

Findings

Algorithm converges reliably on synthetic noisy data

Effective reconstruction despite inverse crime avoidance

Enhanced images demonstrate improved clarity and detail

Abstract

An inverse iterative algorithm for microwave imaging based on moment method solution is presented here. The iterative scheme has been developed on constrained optimization technique and is certain to converge. Different mesh size for the model has been used here to overcome the Inverse Crime. The synthetic data at the receivers is contaminated with different percentage of noise. The ill-posedness of the problem is solved by Levenberg-Marquardt method. The algorithm is applied to synthetic data and the reconstructed image is then further enhanced through the Image enhancement technique