Synergistic super-resolution reconstruction via QR decomposition and POCS

A super-resolution reconstruction method integrating physical priors and numerical optimization is proposed to address the sub-pixel displacement and oversampling information redundancy caused by target-platform relative motion in push-broom imaging. Firstly, the oversampling ratio and optimal super-resolution factor are derived based on target motion velocity and platform parameters. Secondly, stable solutions for ill-posed equations are achieved through regularized QR decomposition of regularized augmented matrices, suppressing noise amplification and artifact generation. Finally, spatiotemporal coupling constraints of imaging systems are embedded via the projection onto convex sets method, realizing detail enhancement and physically consistent reconstruction. Simulation experiments demonstrate that this method improves the local structural similarity index to 0.8402 in infrared remote sensing imagery, while reducing the relative root mean square error of spectral restoration by 80.17, outperforming conventional generalized inverse and iterative interpolation algorithms. The total processing time for 41 spectral channels is better than 150 ms, demonstrating its engineering practical value in hyperspectral data processing.