Design of optimal illumination patterns in single-pixel imaging using image dictionaries
This work addresses efficiency issues in single-pixel imaging, which is incremental as it builds on existing basis patterns.
The paper tackled the problem of long acquisition times in single-pixel imaging by designing adaptive illumination patterns from an image dictionary using PCA, resulting in improved imaging efficiency compared to conventional Fourier SPI.
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.