Enhancing in full-color single-pixel imaging: integrating variable density sampling with hyper-Laplacian priors

Abstract

Full-color single-pixel imaging aims to restore chromatic images using a single detector element, such as a photodiode or a single-pixel camera. However, image quality is inevitably compromised at low sampling rates due to inefficient sampling methods or incomplete representation of spectrum information. To address these challenges, we meticulously consider the distribution of the image frequency spectrum and the correlation between multiple bands and make further improvements in sampling strategy and reconstruction methods. First, we propose a variable density random sampling strategy based on the exponential distribution to enhance image sampling efficiency. Second, we discover that in most cases, there exists a hyper-Laplacian distribution between spectral mixed images and monochromatic images. Building upon this observation, we designed a hyper-Laplacian prior and seamlessly integrated it into our reconstruction method to enhance the performance of full-color images. Experimental results demonstrate that our method significantly improves the quality of reconstructed full-color images compared to state-of-the-art methods.