Adaptive multi-resolution single-pixel imaging based on local transform

Single-pixel imaging involves a trade-off between imaging quality and efficiency due to its sequential detection, particularly challenging for key areas under large frame sizes. We propose a novel mathematical model for multi-resolution single-pixel imaging based on orthogonal local Hadamard transform. To adaptively determine the center of key areas, we employ a dual strategy combining attention maps and a novel sparse geometric moment approach. Our method executes multi-resolution imaging across various levels using multi-resolution pattern sequences. This strategy enhances the imaging quality of key areas while significantly reducing computational burden. Through simulations and experiments, we demonstrate that our adaptive multi-resolution SPI yields superior imaging results in large frame size scenarios and sparse imaging scenes, effectively addressing the quality-efficiency trade-off in single-pixel imaging.