Predicting The Spectrum: Deep Adaptive Sensing for Hadamard Single Pixel Spectral Imaging

The Hadamard Single-Pixel Imaging (HSPI) has emerged as a cost-effective approach for spectral image acquisition, capturing inner products between scenes and coded apertures chosen as rows of the Hadamard matrix. Current strategies employ ordering methodologies to reduce acquisition time by selecting a subset of Hadamard rows. However, they encounter limitations in fixed sensing, particularly in preserving image quality, as each spectral image may have a unique subset of coded apertures that retains the most spatial information. This work tackles these challenges by introducing an adaptive acquisition protocol for HSPI. Our two-step approach seamlessly integrates fixed and adaptive sensing stages, optimizing coded aperture selection through a binary classification model in the 2D Hadamard spectrum. The comparative analysis demonstrates the superiority of our adaptive protocol over existing ordering methodologies, offering a flexible solution to overcome limitations associated with fixed acquisitions.