Limits...
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.

Show MeSH

Related in: MedlinePlus

Critical dimensions of the uniform detector array [8].
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3472830&req=5

f1-sensors-12-10326: Critical dimensions of the uniform detector array [8].

Mentions: In this paper, we consider an IR system with a uniform rectangular detector array, which is illustrated in Figure 1, where a and b are the active region dimensions measured in millimeters (mm) and T1 and T2 are the horizontal and vertical sample spacings. The shaded areas represent the active region of each detector. In this case, the detector model PSF is given by [9]:(4)D(x,y)=1abrect(xa,yb)={1,for/xa/<12and/xb/<120,otherwise


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)

Critical dimensions of the uniform detector array [8].
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-12-10326: Critical dimensions of the uniform detector array [8].
Mentions: In this paper, we consider an IR system with a uniform rectangular detector array, which is illustrated in Figure 1, where a and b are the active region dimensions measured in millimeters (mm) and T1 and T2 are the horizontal and vertical sample spacings. The shaded areas represent the active region of each detector. In this case, the detector model PSF is given by [9]:(4)D(x,y)=1abrect(xa,yb)={1,for/xa/<12and/xb/<120,otherwise

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