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Contrast optimization in broadband passive polarimetric imaging based on color camera

Zijian Guan, François Goudail, Mingxuan Yu, Xiaobo Li, Qun Han, Zhenzhou Cheng, Haofeng Hu, and Tiegen Liu

Open Access Open Access

Abstract

Broadband polarimetric imaging consists of forming an image under spectrally wide illumination after having optimized the polarization state analyzer (PSA) to maximize the target/background discriminability. In previous works, the image sensor was monochrome, and only the intensity contrast was optimized. However, due to its spectrally varying response, the PSA not only changes the light’s intensity, but also its color. This color information can serve as a further parameter to improve discrimination. In this paper, we employ a color camera in a broadband Stokes (passive) polarimetric imaging system and take into color difference’s contribution to discrimination ability in optimizing the PSA setting. We show through experiments that a significant improvement of discrimination ability over monochrome imaging is obtained, especially when there are multiple objects in the scene.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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Supplementary Material (1)

NameDescription
Visualization 1       The variation of the colors of a scene with four different regions with the PSA voltages.

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Figures (7)
Fig. 1
Fig. 1 Quantum efficiencies of RGB channels of the color camera (AVT Stringray F-033C)

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Fig. 2
Fig. 2 (a) The schematic of the experiment setup. (b) Spectrum of the LED light source.

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Fig. 3
Fig. 3 The schematic of the scene containing two regions.

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Fig. 4
Fig. 4 The intensity image of the scene.

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Fig. 5
Fig. 5 Left column: Monochrome polarimetric contrast C, defined in Eq. (8), as a function of (V1, V2) for (a) the scene with 2 regions, (c) the scene with 3 regions, (e) the scene with 4 regions. Contrast values are normalized to 1 in all maps separately. Right column: polarimetric monochrome image with optimal contrast of (b) the scene with 2 regions, (d) the scene with 3 regions, (f) the scene with 4 regions.

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Fig. 6
Fig. 6 Left column: Color polarimetric contrast as a function of (V1, V2) for (a) the scene with 2 regions, (c) the scene with 3 regions, (e) the scene with 4 regions. Contrast values are normalized to 1 in all maps separately. Right column: color polarimetric image with optimal contrast of (b) the scene with 2 regions, (d) the scene with 3 regions, (f) the scene with 4 regions.

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Fig. 7
Fig. 7 (a) Schematic of the coordinates of three objects and the corresponding triangle in RGB color space. (b) Schematic of the coordinates of four objects and the corresponding tetrahedron in RGB color space.

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Equations (9)

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i u = 1 2 T ( V 1 , V 2 ) T S u ,                 u ∈ [ a , b ] ,
T o p t = arg max T ( | i a − i b | ) = arg max ( V 1 - V 2 ) { | 1 2 T( V 1 , V 2 ) T ( S a − S b ) | } .
I u = ∫ 1 2 T ( V 1 , V 2 , λ ) T S u ( λ ) d λ ,                 u ∈ [ a , b ] .
T o p t = arg max T { | I a − I b | } = arg max ( V 1 , V 2 ) { | ∫ 1 2 T( V 1 , V 2 , λ ) T [ S a ( λ ) − S b ( λ ) ] d λ | } .
i k ( V 1 , V 2 ) = ∫ 1 2 Q k ( λ )T( V 1 , V 2 , λ ) T S( λ ) d λ ,         k ∈ [ R , G , B ] .
P i = ( R i , G i , B i ) = ( i R u ( V 1 , V 2 ) , i G u ( V 1 , V 2 ) , i B u ( V 1 , V 2 ) )   ,       u ∈ [ a , b ] .
T o p t ( V 1 , V 2 ) = arg max V 1 , V 2 { ( R a − R b ) 2 + ( G a − G b ) 2 + ( B a − B b ) 2 } .
C = ∏ j = 1 n − 1 ∏ i = j + 1 n d i j ,
d i j = | I i − I j |
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