High-quality correspondence imaging based on sorting and compressive sensing technique

Wu, Heng; Zhang, Xianmin; Gan, Jinqiang; Luo, Chunling; Ge, Peng

doi:10.1088/1612-2011/13/11/115205

Cited by 26 publications

(16 citation statements)

References 26 publications

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“…Compared with the previous CI algorithms, [23][24][25][26][27][28][29][30][31][32][33] the creation process of PN subsets for ASDCI is greatly simplified and the ASDCI is easy to realize whether in hardware or in algorithm. Although a sorting method is also used and a high SNR is achieved in our previous work, 34 no detail method was provided on how to choose the proper number during the PN subset creation. Moreover, the actual applications of this CS-based scheme may be limited by complicated calculations and large memory requirements in CS algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…To further improve the imaging quality of CI and reduce the number of measurements, many CS-based schemes are developed. [31][32][33][34] Although the CS-based schemes can achieve excellent performance, lots of limitations exist in these schemes. For instance, 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.…”

Section: Introductionmentioning

confidence: 99%

“…The reason is that vast numbers of calculations and huge memory requirements bring a tremendous burden on the computer. 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.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive differential correspondence imaging based on sorting technique

Zhang

Shan

et al. 2017

AIP Advances

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive differential correspondence imaging based on sorting technique

Zhang

Shan

et al. 2017

AIP Advances

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“…For example, non-computational 13 – 15 and computational methods have been proposed to increase image quality under low sampling rate 9 – 11 , 16 – 20 . In particular, compressive sensing GI (CSGI) 10 , 16 – 19 and iterative GI 11 , 20 model the problem of image reconstruction in GI as an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based ghost imaging

Lyu

Wang

et al. 2017

Sci Rep

296

141

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In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.

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“…Compliant mechanisms have been widely used in precision engineering applications in which precision of motion, compactness, and feasibility are required. Examples can be easily found in, for example, micro-and nano-manipulation and manufacturing [1][2][3] and optical alignment [4,5]. Compliant mechanisms often refer to monolithic or jointless structures that transfer an input force or displacement to another point through elastic body deformation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Topological and Shape Optimization of Flexure Hinges for Designing Compliant Mechanisms Using the Level Set Method

Zhu

Zhang

Liu

et al. 2019

Chin. J. Mech. Eng.

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A flexure hinge is a major component in designing compliant mechanisms that offers unique possibilities in a wide range of application fields in which high positioning accuracy is required. Although various flexure hinges with different configurations have been successively proposed, they are often designed based on designers' experiences and inspirations. This study presents a systematic method for topological optimization of flexure hinges by using the level set method. Optimization formulations are developed by considering the functional requirements and geometrical constraints of flexure hinges. The functional requirements are first constructed by maximizing the compliance in the desired direction while minimizing the compliances in the other directions. The weighting sum method is used to construct an objective function in which a self-adjust method is used to set the weighting factors. A constraint on the symmetry of the obtained configuration is developed. Several numerical examples are presented to demonstrate the validity of the proposed method. The obtained results reveal that the design of a flexure hinge starting from the topology level can yield more choices for compliant mechanism design and obtain better designs that achieve higher performance.

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High-quality correspondence imaging based on sorting and compressive sensing technique

Cited by 26 publications

References 26 publications

Adaptive differential correspondence imaging based on sorting technique

Adaptive differential correspondence imaging based on sorting technique

Deep-learning-based ghost imaging

Topological and Shape Optimization of Flexure Hinges for Designing Compliant Mechanisms Using the Level Set Method

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