Compressive adaptive computational ghost imaging
This addresses a bottleneck for applications in physics and spectroscopy by enabling faster image acquisition without computational delays.
The paper tackles the problem of high computational overhead in standard compressive sensing techniques, which can take hours to days for image reconstruction, by demonstrating an adaptive compressive sampling method that performs measurements directly in a sparse basis, achieving instant results with much fewer than N^2 measurements.
Compressive sensing is considered a huge breakthrough in signal acquisition. It allows recording an image consisting of $N^2$ pixels using much fewer than $N^2$ measurements if it can be transformed to a basis where most pixels take on negligibly small values. Standard compressive sensing techniques suffer from the computational overhead needed to reconstruct an image with typical computation times between hours and days and are thus not optimal for applications in physics and spectroscopy. We demonstrate an adaptive compressive sampling technique that performs measurements directly in a sparse basis. It needs much fewer than $N^2$ measurements without any computational overhead, so the result is available instantly.