基于自适应阈值方法实现迭代降噪鬼成像

为了有效降低传统鬼成像中相关噪声对成像质量的影响,本文提出一种基于最佳阈值的迭代降噪鬼成像.首先在迭代降噪鬼成像的基础上,采用自适应阈值迭代法,在不需要目标先验信息的前提下,找到一个逼近传统鬼成像中相关噪声的阈值,根据得到的阈值构造噪声干扰项.为了每次迭代初值更接近原始目标的透射系数,对其进行二值化,降低重构图像背景噪声对迭代性能的影响.仿真以及实验结果表明,本文提出的方法与传统方法相比,视觉效果以及峰值信噪比值有明显提高.

Iterative denoising of ghost imaging based on adaptive threshold method

Ghost imaging (GI) is an important technique in the fields of quantum imaging and classical optical imaging, and it can solve the problems which are difficult to solve by the traditional imaging techniques in the optically harsh environments. In this paper, we present the iterative denoising of GI based on an adaptive threshold method. This method is abbreviated as IDGI-AT, which takes the advantages of adaptive threshold, differential, binarization and iterative operation method, and can enhance image quality in GI. In addition, this method can reduce the number of measurements. As is well known, the enormous number of measurements and poor reconstruction quality are obstacles to the engineering application of GI. The correlation noise leads to low signal-to-noise ratio and low imaging efficiency in GI as well. Therefore, we establish a denoising model, which can reduce correlation noise and improve reconstruction quality. We first analyze the iterative denoising of ghost imaging (IDGI) theory, and use the adaptive threshold technique to calculate the ideal threshold associated with the correlation noise. It should be noted that the threshold can be obtained by this method under the condition without requiring prior knowledge of the object. Afterwards, we can construct the correlation noise in this denoising model. In the IDGI, the differential ghost imaging (DGI) image is taken as the initial iteration value. We use the adaptive threshold method, which is different from IDGI, to binarize the initial value of each iteration to make it closer to the original object's transmission coefficient. After three iterations, we can obtain a higher-quality reconstruction image. In order to demonstrate that the IDGI-AT is available, a GI experimental system with a pseudo-thermal light source is set up. The considerable simulation and experimental results show the advantage of our scheme in terms of removing reconstruction image background noise. Especially, the visual effects and peak signal-to-noise ratio values are improved in comparison with those from the traditional GI, DGI and IDGI. Besides, we demonstrate the role of binarization in our scheme. For a binary object, the iterative value binarization can achieve better image quality than that in the case without binarizing the iterative initial value. Therefore, this novel method is likely to provide an alternative mean for GI and further pave the way for the application fields of GI, such as lidar, biomedical engineering, etc.