Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Simple Denoising Method for Novel Speckle-shifting Ghost Imaging with Connected-region Labeling

  • Yuan, Sheng (Department of Information and Engineering, North China University of Water Resources and Electric Power) ;
  • Liu, Xuemei (Department of Information and Engineering, North China University of Water Resources and Electric Power) ;
  • Bing, Pibin (Department of Information and Engineering, North China University of Water Resources and Electric Power)
  • Received : 2018.08.15
  • Accepted : 2019.04.16
  • Published : 2019.06.25
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Abstract

A novel speckle-shifting ghost imaging (SSGI) technique is proposed in this paper. This method can effectively extract the edge of an unknown object without achieving its clear ghost image beforehand. However, owing to the imaging mechanism of SSGI, the imaging result generally contains serious noise. To solve the problem, we further propose a simple and effective method to remove noise from the speckle-shifting ghost image with a connected-region labeling (CRL) algorithm. In this method, two ghost images of an object are first generated according to SSGI. A threshold and the CRL are then used to remove noise from the imaging results in turn. This method can retrieve a high-quality image of an object with fewer measurements. Numerical simulations are carried out to verify the feasibility and effectiveness.

Keywords

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FIG. 1. Schematic diagram of the edge-detection system based on SSGI.

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FIG. 2. Numerical simulation results for the novel and conventional SSGI techniques: (a) original image, (b) edge directly extracted from the object with the Sobel operator, (c) and (d) the horizontal and vertical intensity patterns, (e) and (f) the extracted horizontal and vertical edges, (g) and (h) the imaging results for the novel and conventional SSGI respectively.

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FIG. 3. Flow chart for the denoising method.

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FIG. 4. (a) The other object image, and (b) its edge, directly extracted with the Sobel operator.

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FIG. 5. Numerical simulation results for our proposed method in this paper, for number of measurements M = 20000 and coefficient ∊ = 0.01 in CRL.

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FIG. 6. Simulation result for a color image: (a) original image, (b) edge extracted with the proposed method.

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FIG. 7. Numerical simulation results for ((a) and (b)) SPSGI and ((c) and (d)) DFSI.

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FIG. 8. The curves for PSNR varying with the number of measurements in SSGI, DFSI, SPSGI, and our proposed method, for the binary image.

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