Temporally and spatially variant-resolution illumination patterns in computational ghost imaging

Abstract

Conventional computational ghost imaging (CGI) uses light that carries a sequence of patterns with uniform resolution to illuminate an object and then performs correlation calculations based on the light intensity value reflected by the object and the preset patterns to obtain object images. CGI requires numerous measurements to obtain high-quality images, especially if high-resolution images are to be obtained. To solve this problem, we developed temporally variable-resolution illumination patterns, replacing the conventional uniform-resolution illumination patterns with a sequence of patterns of different imaging resolutions. In addition, we propose combining temporally variable-resolution illumination patterns and spatially variable-resolution structures to develop temporally and spatially variable-resolution (TSV) illumination patterns, which not only improves the imaging quality of the region of interest (ROI), but also reduces the effect of noise, making up for the shortcomings of temporally variable-resolution patterns. The methods using the proposed illumination patterns are verified by simulations and experiments compared with uniform-resolution computational ghost imaging (UCGI). For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than UCGI but is less robust to noise. The method using TSV illumination patterns has better imaging quality in the ROI than the method using temporally variable-resolution illumination patterns and UCGI with the same number of measurements. We also experimentally verify that the method using TSV patterns has better imaging performance when applied to higher resolution imaging. The proposed methods are expected to solve the current CGI problem that hinders high-resolution and high-quality imaging.