Programmable Single-Pixel Imaging

Abstract

Conventional imaging sensors often reach their limits in terms of resolution and dynamic range. In addition, conventional imaging in invisible wavelengths is relatively more expensive and complicated. As an alternative, single-pixel cameras allow reduction of cost and complexity that would be otherwise required in a conventional multi-nixel camera. In terms of digital imaging, Nyquist-Shannon theorem states that to stably recover an image without introducing perceptible errors, the number of measurements and the number of image pixels are required to be at least the same. As the number of image pixels is ever increasing, increasing the number of measurements to fulfill Nyquist-Shannon theorem's requirements has become increasingly challenging. Since in many cases increasing the number of measurements means that the cost and time required are increasing accordingly as well. Therefore a mean to recover images from a number of measurements less than the number of pixels (sub-Nyquist measurements) is needed. The objective of this paper is to present and compare single-pixel imaging via compressive sensing/sampling (CS) and spatially-variant resolution (SVR) single-pixel imaging. Both methods are capable of recovering images stably from sub-Nyquist measurements. The measurements and reconstructions of images were done in simulations. SVR single-pixel imaging reduces the number of measurement by sacrificing the resolution of the peripheral regions. This realizes the programmable imaging concept where multi-resolution can be adaptively applied to optimize the balance between image quality and number of measurement. This could benefit some imaging applications where a target in the region of interest (the fovea) is prioritized over the rest of the region (such as the background).