Edge detection based on gradient ghost imaging

Liu, Xuefeng; Yao, Xu‐Ri; Lan, Ruo‐Ming; Wang, Chao; Zhai, Guangtao

doi:10.1364/oe.23.033802

Cited by 60 publications

(31 citation statements)

References 24 publications

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“…[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.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Adaptive differential correspondence imaging based on sorting technique

Zhang

Shan

et al. 2017

AIP Advances

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“…The algorithms are complicated and time consuming. Recently, an optical imaging system based on gradient ghost image (GGI) was demonstrated to realize the edge detection in the image acquisition stage by controlling the distribution of the light field and finding out the gradient of the object for an improved signal‐to‐noise ratio of the edges . However, GGI technique is unable to capture fast dynamical phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Ultrafast cell edge detection by line‐scan time‐stretch microscopy

Dai

Zheng

et al. 2018

Journal of Biophotonics

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Ultrafast time-stretch imaging technique recently attracts an increasing interest for applications in cell classification due to high throughput and high sensitivity. A novel imaging modality of time-stretch imaging technique for edge detection is proposed. Edge detection based on the directional derivative is realized using differential detection. As the image processing is mainly implemented in the physical layer, the computation complexity of edge extraction is significantly reduced. An imaging system for edge detection with the scan rate of 77.76 MHz is experimentally demonstrated. Resolution target is first measured to verify the feasibility of the edge extraction. Furthermore, various cells, including red blood cells, lung cancer cells and breast cancer cells, are detected. The edges of cancerous cells present in a completely different form. The imaging system for edge detection would be a good candidate for high-throughput cell classification.

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“…Human vision is usually through the target object's edge and contour [1], to distinguish the target objects [2][3], whereas in the multifunctional digital ship image, the edge is an important feature that distinguishes different regions [4][5]. The edge and contour information of multifunctional digital ship image refers to the part of that the local brightness changes significantly [6], it exists between the target image and another target image [7], the target image and background image, the regional image and another regional image [8][9].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on the Method of Extracting Edge and Contour Information of Multifunctional Digital Ship Image

Yin

2017

Polish Maritime Research

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The result of the extraction of the edge and contour information of the multifunctional digital ship image directly affects the evaluation and recognition of the subsequent image quality. At present, the common method used to extract the edge contour information is based on the Canny operator, and there is a problem that the edge is not clear.In order to obtain more accurate edge information, a method of extracting edge and contour information of multimedia digital image based on multi-scale morphology is proposed. Firstly, the digital ship image is made double filter and the fuzzy threshold segmentation, and then the edge and contour information is extracted by multi-scale morphology. Experimentsshow that the proposed method can obtain more accurate edge information compared with the other methods.

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Edge detection based on gradient ghost imaging

Cited by 60 publications

References 24 publications

Adaptive differential correspondence imaging based on sorting technique

Adaptive differential correspondence imaging based on sorting technique

Ultrafast cell edge detection by line‐scan time‐stretch microscopy

A Comparative Study on the Method of Extracting Edge and Contour Information of Multifunctional Digital Ship Image

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