Demosaicking DoFP images using Newton’s polynomial interpolation and polarization difference model

Li, Ning; Zhao, Yongqiang; Pan, Quan; Kong, Seong G.

doi:10.1364/oe.27.001376

Cited by 93 publications

(52 citation statements)

References 36 publications

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“…In the previous research and applications to polarimetric imaging, there are mainly three AoP data visualization methods, as shown in Figure 1. In the first method, the grey level value is used to characterize the AoP, with the state from dark to bright in the image indicating a range from 0 • to 180 • [21]. In the second method, various hues from blue to red are used to indicate the AoP, assuming that the saturation is 1 and the intensity is 1 [26].…”

Section: Polarization Parameters and Aop Visualization Mapping Functionmentioning

confidence: 99%

“…Specifically, the division-of-focal-plane (DoFP) polarization sensor significantly decreases the system complexity of the polarimetric imaging system, improves the efficiency of polarization testing, and reduces the cost of the apparatus. Therefore, this technology becomes popular and has caused a lot of research in recent years [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…The results of previous AoP data visualization method: (a) Method 1[21]; (b) Method 2[26]; (c) Method 3[27].…”

mentioning

confidence: 99%

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An Angle of Polarization (AoP) Visualization Method for DoFP Polarization Image Sensors Based on Three Dimensional HSI Color Space

Wang

et al. 2019

Sensors

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A demand for division of focal plane (DoFP) polarization image sensors grows rapidly as nanofabrication technologies become mature. The DoFP sensor can output real time data of polarization information. In this paper, a novel visualization method for angle of polarization (AoP) is proposed for DoFP polarization image sensors. The data characteristics of AoP are analyzed, and strategies for a visualization method are proposed which conforms to the characteristics of AoP data. According to these strategies, we propose a visualization method for AoP data based on three dimensional HSI color space. This method uses intensity and saturation to characterize the magnitude of the angle between the polarization direction and the horizontal direction wherein the hue indicates the deflection direction. It is shown by the numerical simulation that the noise in the AoP image can be suppressed by our visualization method. In addition, the real-world experiment results are consistent with the numerical simulation and verify that the AoP image obtained by our method can suppress the influence of characterization noise, and the image is simple and intuitive, which is advantageous to human vision. The proposed method can be directly used for the commercialized DoFP polarization image sensor to display real-time AoP data.

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Section: Polarization Parameters and Aop Visualization Mapping Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Angle of Polarization (AoP) Visualization Method for DoFP Polarization Image Sensors Based on Three Dimensional HSI Color Space

Wang

et al. 2019

Sensors

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“…To tackle polarization demosaicking problems, various interpolation methods have been proposed, e.g. bilinear, bicubic, cubic spline interpolation [GG11], Fourier domain demosaicking [TLR09], Intensity Correlation among Polarization Channels [ZLHC16], Sparse Representation-based Demosaicing [ZLL*18], Newton's Polynomial Interpolation [LZPK19] and End-to-end Fully-Convolutional Neural Network [ZLZY19]. We refer to the above methods for a complete summary.…”

Section: Introductionmentioning

confidence: 99%

Linear Polarization Demosaicking for Monochrome and Colour Polarization Focal Plane Arrays

Qiu

Wang

et al. 2021

Computer Graphics Forum

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Division-of-focal-plane (DoFP) polarization image sensors allow for snapshot imaging of linear polarization effects with inexpensive and straightforward setups. However, conventional interpolation based image reconstruction methods for such sensors produce unreliable and noisy estimates of quantities such as Degree of Linear Polarization (DoLP) or Angle of Linear Polarization (AoLP). In this paper, we propose a polarization demosaicking algorithm by inverting the polarization image formation model for both monochrome and colour DoFP cameras. Compared to previous interpolation methods, our approach can significantly reduce noise induced artefacts and drastically increase the accuracy in estimating polarization states. We evaluate and demonstrate the performance of the methods on a new high-resolution colour polarization dataset. Simulation and experimental results show that the proposed reconstruction and analysis tools offer an effective solution to polarization imaging.

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“…Note that various interpolation methods have been recently proposed to improve both the spatial resolution and the accuracy of the polarization information, due to the sensors’ spatially modulated arrangement of a micro-polarization array. These include bilinear, bicubic, bicubic spline, and gradient-based interpolation methods [ 32 , 33 , 34 ], new micro-polarizer array patterns-used interpolation method [ 35 , 36 ], spatio-temporal channeled approach [ 37 ], smoothness-based interpolation method [ 38 ], Newton’s polynomials based interpolation [ 39 ], correlation-based interpolation method [ 40 ], deep convolutional neural network-based polarization demosaicing [ 41 , 42 ], minimized Laplacian polarization residual interpolation [ 43 ], sparse representation-based demosaicing method [ 44 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Polarization-Sensitive and Wide Incidence Angle-Insensitive Fabry–Perot Optical Cavity Bounded by Two Metal Grating Layers

Hwang

Jeon

et al. 2020

Sensors

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Infrared (IR) polarimetric imaging has attracted attention as a promising technology in many fields. Generally, superpixels consisting of linear polarizer elements at different angles plus IR imaging array are used to obtain the polarized target signature by using the detected polarization-sensitive intensities. However, the spatial arrangement of superpixels across the imaging array may lead to an incorrect polarimetric signature of a target, due to the range of angles from which the incident radiation can be collected by the detector. In this article, we demonstrate the effect of the incident angle on the polarization performance of an alternative structure where a dielectric layer is inserted between the nanoimprinted subwavelength grating layers. The well-designed spacer creates the Fabry–Perot cavity resonance, and thereby, the intensity of transverse-magnetic I-polarized light transmitted through two metal grating layers is increased as compared with a single-layer metal grating, whereas transverse-electric (TE)-transmitted light intensity is decreased. TM-transmittance and polarization extinction ratio (PER) of normally incident light of wavelength 4.5 μm are obtained with 0.49 and 132, respectively, as the performance of the stacked subwavelength gratings. The relative change of the PERs for nanoimprint-lithographically fabricated double-layer grating samples that are less than 6% at an angle of incidence up to 25°, as compared to the normal incidence. Our work can pave the way for practical and efficient polarization-sensitive elements, which are useful for many IR polarimetric imaging applications.

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Demosaicking DoFP images using Newton’s polynomial interpolation and polarization difference model

Cited by 93 publications

References 36 publications

An Angle of Polarization (AoP) Visualization Method for DoFP Polarization Image Sensors Based on Three Dimensional HSI Color Space

An Angle of Polarization (AoP) Visualization Method for DoFP Polarization Image Sensors Based on Three Dimensional HSI Color Space

Linear Polarization Demosaicking for Monochrome and Colour Polarization Focal Plane Arrays

Polarization-Sensitive and Wide Incidence Angle-Insensitive Fabry–Perot Optical Cavity Bounded by Two Metal Grating Layers

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