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

TL;DR

This paper introduces temporally and spatially variable-resolution illumination patterns in computational ghost imaging to enhance image quality and robustness, especially for high-resolution and region-specific imaging, verified through simulations and experiments.

Contribution

The paper proposes novel temporally and spatially variable-resolution illumination patterns to improve computational ghost imaging performance over traditional uniform-resolution methods.

Findings

TSV patterns improve ROI imaging quality

Temporally variable-resolution patterns outperform CGI in image quality

TSV patterns are effective for high-resolution imaging

Abstract

Conventional computational ghost imaging (CGI) uses light carrying a sequence of patterns with uniform-resolution to illuminate the object, then performs correlation calculation based on the light intensity value reflected by the target and the preset patterns to obtain object image. It requires a large number of 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 to combine temporally variable-resolution illumination patterns and spatially variable-resolution structure to develop temporally and spatially variable-resolution (TSV) illumination patterns, which not only improve the imaging quality of the region of interest (ROI) but also improve the robustness to noise. The methods using proposed illumination patterns are verified by simulations and experiments compared with CGI. For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than CGI, but it is less robust to noise. The method using TSV illumination patterns has better imaging quality in ROI than the method using temporally variable-resolution illumination patterns and CGI under the same number of measurements. We also experimentally verify that the method using TSV patterns have better imaging performance when applied to higher resolution imaging. The proposed methods are expected to solve the current computational ghost imaging that is difficult to achieve high-resolution and high-quality imaging.