Re-ordering of Hadamard matrix using Fourier transform and gray-level co-occurrence matrix for compressive single-pixel imaging

TL;DR

This paper introduces two new Hadamard basis orders, AS and AI, which improve image reconstruction quality in single-pixel imaging, especially at higher sampling ratios, by leveraging Fourier transform and gray-level co-occurrence matrix analysis.

Contribution

The paper proposes two novel Hadamard basis orders, AS and AI, and demonstrates their superior performance over existing orders in single-pixel imaging through simulations and experiments.

Findings

AS and AI orders outperform existing methods at higher sampling ratios.

Peak signal-to-noise ratio is unreliable for image quality assessment in this context.

New orders achieve higher structure similarity index values than CC order.

Abstract

One of the most active research fields in single-pixel imaging is the influence of the sampling basis and its order in the quality of the reconstructed images. This paper presents two new orders, ascending scale (AS) and ascending inertia (AI), of the Hadamard basis and test their performance, using simulation and experimental methods, for low sampling ratios (0.5 to 0.01). These orders were compared with two state-of-the-art orders, cake-cutting (CC) and total gradient (TG), using TVAL3 as the reconstruction algorithm and three noise levels. These newly proposed orders have better reconstructed image quality on the simulation data set (110 images) and achieved structure similarity index values higher than CC order. The experimental data set (2 images) showed that the AS and AI orders performed better with a sampling ratio of 0.5, while for lower sampling ratio the performance of AS, AI and CC was similar. The TG order performed worst in the majority of the cases. Finally, the simulation results present clear evidence that peak signal-to-noise ratio (PSNR) is not a reliable image quality assessment (IQA) metric to assess image reconstruction quality in the context of single pixel imaging.