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.
Highlights
The four primary physical characteristics of light are intensity, wavelength, coherence, and polarization
Current polarization imaging sensors are mainly categorized into division of time (DoT), division of amplitude, division of aperture, and division of focal plane (DoFP) [7,8,9,10]
Visual comparison and peak signal to noise ratio (PSNR) analysis are applied to the simulated DoFP images
Summary
The four primary physical characteristics of light are intensity, wavelength, coherence, and polarization. Conventional imaging sensors can only record the intensity and wavelength as brightness and color respectively. Polarization imaging is an emerging technology and it can provide extra useful information. It has been widely applied in the fields of material classification [1], fog de-hazing [2], 3D shape reconstruction [3], and biomedical imaging [4,5,6]. DoFP polarimeters are composed of a collection of pixelated polarizers aligned in four different orientations for 2-by-2 pixels. DoFP polarimeters are inherently temporally synchronized and all four polarized image are well-aligned. DoFP polarimeters have attracted increasing attention in recent years because of these advantages
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