Improving spatial domain based image formation through compressed sensing

TL;DR

This paper enhances image reconstruction in single-pixel systems by optimizing detector field of view and employing compressed sensing, leading to improved image quality over traditional interpolation methods.

Contribution

It introduces a multi-level sampling approach with dynamic detector adjustments and demonstrates its superiority over uniform sampling and interpolation in image quality.

Findings

Multi-level sampling improves PSNR distribution.

Compressed sensing outperforms Lanczos interpolation.

Dynamic detector adjustment enhances image reconstruction.

Abstract

In this paper, we improve image reconstruction in a single-pixel scanning system by selecting an detector optimal field of view. Image reconstruction is based on compressed sensing and image quality is compared to interpolated staring arrays. The image quality comparisons use a "dead leaves" data set, Bayesian estimation and the Peak-Signal-to-Noise Ratio (PSNR) measure. Compressed sensing is explored as an interpolation algorithm and shows with high probability an improved performance compared to Lanczos interpolation. Furthermore, multi-level sampling in a single-pixel scanning system is simulated by dynamically altering the detector field of view. It was shown that multi-level sampling improves the distribution of the Peak-Signal-to-Noise Ratio. We further explore the expected sampling level distributions and PSNR distributions for multi-level sampling. The PSNR distribution indicates that there is a small set of levels which will improve image quality over interpolated staring arrays. We further conclude that multi-level sampling will outperform single-level uniform random sampling on average.