基于主成分分析的焦平面偏振计分割去噪方法。

PCA-based denoising method for division of focal plane polarimeters.

作者信息

Zhang Junchao, Luo Haibo, Liang Rongguang, Zhou Wei, Hui Bin, Chang Zheng

出版信息

Opt Express. 2017 Feb 6;25(3):2391-2400. doi: 10.1364/OE.25.002391.

DOI:10.1364/OE.25.002391

PMID:29519085

Abstract

Division of focal plane (DoFP) polarimeters are composed of interlaced linear polarizers overlaid upon a focal plane array sensor. The interpolation is essential to reconstruct polarization information. However, current interpolation methods are based on the unrealistic assumption of noise-free images. Thus, it is advantageous to carry out denoising before interpolation. In this paper, we propose a principle component analysis (PCA) based denoising method, which works directly on DoFP images. Both simulated and real DoFP images are used to evaluate the denoising performance. Experimental results show that the proposed method can effectively suppress noise while preserving edges.

摘要

焦平面划分（DoFP）偏振计由交错排列在焦平面阵列传感器上的线性偏振器组成。插值对于重建偏振信息至关重要。然而，当前的插值方法基于无噪声图像这一不切实际的假设。因此，在插值之前进行去噪是有利的。在本文中，我们提出一种基于主成分分析（PCA）的去噪方法，该方法直接作用于DoFP图像。使用模拟和真实的DoFP图像来评估去噪性能。实验结果表明，所提出的方法能够在保留边缘的同时有效抑制噪声。