A sparse coding approach to inverse problems with application to microwave tomography

TL;DR

This paper introduces a sparse coding approach to solve ill-posed inverse problems, specifically applied to microwave tomography, leveraging natural image representations to improve reconstruction quality.

Contribution

It extends sparse coding techniques to non-linear, ill-posed inverse problems in microwave tomography, enhancing existing reconstruction algorithms.

Findings

Improved image reconstruction accuracy in microwave tomography.

Effective use of natural image sparse representations for inverse problems.

Potential for significant advancements over current methods.

Abstract

Inverse imaging problems that are ill-posed can be encountered across multiple domains of science and technology, ranging from medical diagnosis to astronomical studies. To reconstruct images from incomplete and distorted data, it is necessary to create algorithms that can take into account both, the physical mechanisms responsible for generating these measurements and the intrinsic characteristics of the images being analyzed. In this work, the sparse representation of images is reviewed, which is a realistic, compact and effective generative model for natural images inspired by the visual system of mammals. It enables us to address ill-posed linear inverse problems by training the model on a vast collection of images. Moreover, we extend the application of sparse coding to solve the non-linear and ill-posed problem in microwave tomography imaging, which could lead to a significant improvement of the state-of-the-arts algorithms.