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Multi-sensor fusion of infrared and electro-optic signals for high resolution night images.

Huang X, Netravali R, Man H, Lawrence V - Sensors (Basel) (2012)

Bottom Line: The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair.Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images.Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA. xhuang3@stevens.edu

ABSTRACT
Electro-optic (EO) image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR) image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF) proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

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Related in: MedlinePlus

Examples of IRE image transformation using the designed inverse filter. (a) Original IR Image 1; (b) Transformed IR Image 1 via the inverse filter; (c) Original IR Image 2; (d) Transformed IR Image 2 via the inverse filter.
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f3-sensors-12-10326: Examples of IRE image transformation using the designed inverse filter. (a) Original IR Image 1; (b) Transformed IR Image 1 via the inverse filter; (c) Original IR Image 2; (d) Transformed IR Image 2 via the inverse filter.

Mentions: In this paper, we assume the proposed theoretical PSF to be H (u, v) in the designed inverse filter. If we let the original IR image pass through the designed inverse filter, then we can obtain a transformed IR image with temperature information of the objects. Figure 3 shows two IR image transformation examples using the designed inverse filter.


Multi-sensor fusion of infrared and electro-optic signals for high resolution night images.

Huang X, Netravali R, Man H, Lawrence V - Sensors (Basel) (2012)

Examples of IRE image transformation using the designed inverse filter. (a) Original IR Image 1; (b) Transformed IR Image 1 via the inverse filter; (c) Original IR Image 2; (d) Transformed IR Image 2 via the inverse filter.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472830&req=5

f3-sensors-12-10326: Examples of IRE image transformation using the designed inverse filter. (a) Original IR Image 1; (b) Transformed IR Image 1 via the inverse filter; (c) Original IR Image 2; (d) Transformed IR Image 2 via the inverse filter.
Mentions: In this paper, we assume the proposed theoretical PSF to be H (u, v) in the designed inverse filter. If we let the original IR image pass through the designed inverse filter, then we can obtain a transformed IR image with temperature information of the objects. Figure 3 shows two IR image transformation examples using the designed inverse filter.

Bottom Line: The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair.Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images.Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA. xhuang3@stevens.edu

ABSTRACT
Electro-optic (EO) image sensors exhibit the properties of high resolution and low noise level at daytime, but they do not work in dark environments. Infrared (IR) image sensors exhibit poor resolution and cannot separate objects with similar temperature. Therefore, we propose a novel framework of IR image enhancement based on the information (e.g., edge) from EO images, which improves the resolution of IR images and helps us distinguish objects at night. Our framework superimposing/blending the edges of the EO image onto the corresponding transformed IR image improves their resolution. In this framework, we adopt the theoretical point spread function (PSF) proposed by Hardie et al. for the IR image, which has the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we design an inverse filter for the proposed PSF and use it for the IR image transformation. The framework requires four main steps: (1) inverse filter-based IR image transformation; (2) EO image edge detection; (3) registration; and (4) blending/superimposing of the obtained image pair. Simulation results show both blended and superimposed IR images, and demonstrate that blended IR images have better quality over the superimposed images. Additionally, based on the same steps, simulation result shows a blended IR image of better quality when only the original IR image is available.

Show MeSH
Related in: MedlinePlus