Enhancing critical resolution of a ghost imaging system by using a vortex beam

Abstract

In an imaging system, resolution and signal-to-noise ratio (SNR) are two important indexes to characterize imaging quality. Ghost imaging is a novel imaging method whose imaging resolution and SNR are affected by the speckle size. In this paper, the relation between speckle size and resolution as well as that between speckle size and SNR in the GI system is analyzed in detail. It is shown that the critical resolution, resolvable minimum-separation between two adjacent objects, is approximately equal to the speckle size (speckle diameter). There exists an optimum SNR when the speckle size is larger than the object size. Based on our conclusion, we propose a scheme to enhance the critical resolution of the GI system by using a vortex beam, and the enhancement ability under different topological charges is clearly presented, which can be quantized by a simple formula.

Supplementary document for Computational Plenoptic Ghost Diffraction - 6292774.pdf

“…S3 and S4. Meanwhile, an evaluation parameter, R value [8][9][10], is introduced to quantitatively show the resolution performance of the reconstructed double-slit pattern, in which R is defined as the ratio of the intensity at the center to the maximum intensity of the reconstructed double-slit pattern. For example, Figures.…”

Ghost Imaging Through a Supersonic Wind‐Induced Environment Under Weak Illumination

“…In order to measure the low light detection, the intensity ratio α between the signal and noise was introduced and defined as detection SNR [ 24 ] : $$$$\begin{equation}\alpha = \frac{{\left\langle I \right\rangle - \left\langle {{I}_n} \right\rangle }}{{\left\langle {{I}_n} \right\rangle }}\end{equation}$$$$where I is the sum of the detected intensity captured by the CCD, $${I}_n$$ is the noise intensity, $$\langle I \rangle - \langle {{I}_n} \rangle $$ and $$\langle {{I}_n} \rangle $$ denote the average detected signal and average noise intensity, respectively. The SNR of the reconstructed object is defined as [ 25,26 ] : $$$$\begin{equation}{\mathrm{SNR}} = \frac{{\left\langle {{G}_s} \right\rangle - \left\langle {{G}_n} \right\rangle }}{{\sqrt {0.5\left( {{D}_s + {D}_n} \right)} }}\end{equation}$$$$where $${G}_s$$ and $${G}_n$$ are the ensemble average of the reconstructed object signal with the transmission being one and zero, respectively, and $${D}_s$$ and $${D}_n$$ are the corresponding variances.…”

“…where I is the sum of the detected intensity captured by the CCD, I n is the noise intensity, ⟨I⟩ − ⟨I n ⟩ and ⟨I n ⟩ denote the average detected signal and average noise intensity, respectively. The SNR of the reconstructed object is defined as [25,26] :…”