Anti-scattering medium computational ghost imaging with modified Hadamard patterns

Illumination patterns of computational ghost imaging (CGI) systems suffer from reduced contrast when passing through a scattering medium, which causes the effective information in the reconstruction result to be drowned out by noise. A two-dimensional (2D) Gaussian filter performs linear smoothing operation on the whole image for image denoising. It can be combined with linear reconstruction algorithms of CGI to obtain the noise-reduced results directly, without post-processing. However, it results in blurred image edges while performing denoising and, in addition, a suitable standard deviation is difficult to choose in advance, especially in an unknown scattering environment. In this work, we subtly exploit the characteristics of CGI to solve these two problems very well. A kind of modified Hadamard pattern based on the 2D Gaussian filter and the differential operation features of Hadamard-based CGI is developed. We analyze and demonstrate that using Hadamard patterns for illumination but using our developed modified Hadamard patterns for reconstruction (MHCGI) can enhance the robustness of CGI against turbid scattering medium. Our method not only helps directly obtain noise-reduced results without blurred edges but also requires only an approximate standard deviation, i.e., it can be set in advance. The experimental results on transmitted and reflected targets demonstrate the feasibility of our method. Our method helps to promote the practical application of CGI in the scattering environment.

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

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.