Design of optimal illumination patterns in single-pixel imaging using image dictionaries

TL;DR

This paper introduces an adaptive method for designing illumination patterns in single-pixel imaging using PCA-based image dictionaries, significantly improving imaging efficiency over traditional fixed basis patterns.

Contribution

It proposes a novel adaptive scheme for optimizing illumination patterns in SPI by leveraging PCA-derived image dictionaries, enhancing efficiency.

Findings

Outperforms Fourier SPI in imaging efficiency

Uses PCA to extract common image features

Demonstrates effectiveness through simulations and experiments

Abstract

Single-pixel imaging (SPI) has a major drawback that many sequential illuminations are required for capturing one single image with long acquisition time. Basis illumination patterns such as Fourier patterns and Hadamard patterns can achieve much better imaging efficiency than random patterns. But the performance is still sub-optimal since the basis patterns are fixed and non-adaptive for varying object images. This Letter proposes a novel scheme for designing and optimizing the illumination patterns adaptively from an image dictionary by extracting the common image features using principal component analysis (PCA). Simulation and experimental results reveal that our proposed scheme outperforms conventional Fourier SPI in terms of imaging efficiency.