Polarization denoising method for single photon counting imaging

A single-photon counting and imaging technology, applied in computing, image enhancement, complex mathematical operations, etc., can solve problems that cannot easily reduce the impact of environmental noise

Active Publication Date: 2020-11-06
WUHAN UNIV
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Problems solved by technology

In the case of low signal-to-noise ratio (SNR), traditional denois

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  • Polarization denoising method for single photon counting imaging
  • Polarization denoising method for single photon counting imaging
  • Polarization denoising method for single photon counting imaging

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Embodiment Construction

[0070] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0071] Such as figure 1 As shown, this embodiment provides a polarization denoising method for realizing single photon counting imaging in a high background noise state, which specifically includes the following steps:

[0072] Step 1: Calculate the total intensity detected by the detector in two directions through the Stokes vector and the Muller matrix, and define a new reflectance index α N ;

[0073] The traditional single-photon counting imaging system is generally composed of a laser light source, an optical transceiver scanning system, a single-photon detector, a time-dependent single-photon technology module, and a data processing and system control module. Periodicity is used by raster scanning. The pulsed laser illuminates the scene, the re...

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Abstract

The invention provides a polarization denoising method for single photon counting imaging, which comprises the following steps of: calculating the total intensity detected by detectors in two directions through a stokes vector and a Mueller matrix, and defining a new reflectivity index alpha N; constructing a single photon counting imaging polarization denoising model by calculating a negative logarithm likelihood function of the reflectivity alpha and the linear depolarization parameter beta and adding a non-local prior sparse representation constraint; and by solving the optimization problemthrough an alternating minimization method, obtaining and outputting a reflectivity and depolarization parameter image. According to the invention, in a high background noise environment, the polarization difference between signal light and background light is utilized, so the signal-to-noise ratio is improved fundamentally, and meanwhile, non-local prior sparse representation constraints are combined, so that not only can a high-quality reflectivity image reflecting target features be obtained, but also a linear depolarization parameter image of a target can be obtained, and polarization information of the target is obtained.

Description

Technical field [0001] The invention belongs to the field of single-photon counting imaging and relates to a denoising method for single-photon counting imaging, which is suitable for most active imaging systems such as radar and other application scenarios. Background technique [0002] Single-photon counting imaging technology is a technology that obtains target information by counting the photons reflected in the scene where the target is detected, and then reconstructs the target image. It has attracted widespread attention in the fields of remote sensing and space detection. Different from traditional imaging technology, the photodetector used in single photon counting imaging technology is a Geiger mode avalanche photodiode (GM-APD) with single photon detection capability. Therefore, it can detect objects under extremely low light levels. However, in low light conditions, noise will greatly affect the image reconstruction quality. Traditional denoising methods such as non...

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Application Information

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IPC IPC(8): G01J11/00G01S17/89G01S7/483G06F17/11G06F17/16G06T5/00
CPCG01J11/00G01S7/483G01S17/89G06F17/11G06F17/16G06T5/002
Inventor 田昕陈葳李松朱凯余典杨晋陵马跃周辉
Owner WUHAN UNIV
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