Neural Network Hyperspectral Reconstruction

The patent applies preliminary action by performing view difference correction on the captured images before the hyperspectral reconstruction process. The machine learning model pre-processes the spectrally sheared and unsheared images to correct misalignments and view differences, ensuring that the subsequent reconstruction algorithm receives pre-aligned input data. This preliminary correction step eliminates motion artifacts and alignment issues before reconstruction begins, resolving the contradiction between capturing accurate spectral information and minimizing imaging time.

The patent introduces an intermediary machine learning-based view difference correction step that acts as a mediator between the captured spectrally sheared images and the final hyperspectral reconstruction. This intermediary process corrects view differences and aligns images without requiring complex multiplexing hardware, thereby maintaining spatial resolution while achieving single-shot capture. The correction algorithm serves as a computational intermediary that preserves spatial information while enabling time-efficient imaging.

The patent replaces traditional mechanical alignment processes with a machine learning-based computational approach. Instead of using physical alignment mechanisms or complex optical adjustment systems, the invention employs a neural network model that automatically learns and corrects view differences between spectrally sheared and unsheared images. This substitution of mechanical alignment with intelligent algorithms maintains high alignment accuracy while dramatically reducing processing time and cost, directly resolving the contradiction between alignment precision and reconstruction speed.