Abstract
We develop an adaptive differential correspondence imaging (CI) method using a sorting technique. Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS) are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI) setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data.
Highlights
In adaptive SDCI (ASDCI), only differential, sorting and adaptive approaches are employed during the positive and negative (PN) subset generation process, and only part of the matrices (2Nopt frames) and addition and subtraction operations are used in the image reconstruction
We have demonstrated that by using sorting and adaptive methods in a computational differential correspondence imaging (CI) algorithm, one can recover the object image with a high quality using only parts of the measurement data. This method can dramatically reduce the noise compared with the traditional GI (TGI) and TCI, and shows better performance than the conventional differential GI (DGI), normalized GI (NGI) and DCI in the experiments
The ASDCI method is simpler than the TGI, DGI and NGI schemes because of its unique imaging mechanism
Summary
Ghost imaging (GI) is a novel imaging technique that can reconstruct the image of an object using intensity correlation of two light beams.[1,2,3,4,5] Since Pittman et al accomplished the first GI experiment,[1] the GI technique gradually transforms from the theory to practice and yields many applications, such as image processing,[6] remote sensing,7 3D reconstruction,[8] and so on.[9,10,11,12] In recent years, many improved GI schemes have been developed, including iterative GI,[13] differential GI (DGI),[14] normalized GI (NGI)[15] and others.[16,17,18,19,20] In GI, DGI or NGI schemes, one has to conduct large amounts of correlation calculations between the bucket detector signals and the corresponding reference detector signals.[1,2,3,4,5,13,14,15,16,17,18,19] This procedure is very time consuming, especially in the high quality GI cases, where the number of measurements is extremely large. The CS-based schemes may not work well in the real-time and high-quality situations, especially in the situation that the image to be reconstructed is very large and complicated. In our previous work,[34] we proposed a high-quality CI method based on sorting and CS techniques. This method can dramatically improve the imaging quality of CI and reduce the measurements. Different from the above mentioned methods, we use a sorting and adaptive method in the differential CI algorithm for the image reconstruction This scheme, which we call ASDCI, takes the advantages of the adaptive, sorting and differential technique, and can further reduce the computation cost in CI and well overcome the limitations in the CS-based schemes. In SPR technique, the images are obtained by the imaging sensor
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