TY - JOUR
T1 - Multi-sensor fusion of infrared and electro-optic signals for high resolution night images
AU - Huang, Xiaopeng
AU - Netravali, Ravi
AU - Man, Hong
AU - Lawrence, Victor
PY - 2012/8
Y1 - 2012/8
N2 - 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.
AB - 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.
KW - EO image edge detection
KW - IR image transformation
KW - Image pair blending/superimposing
KW - Inverse filter design
KW - Theoretical PSF
UR - http://www.scopus.com/inward/record.url?scp=84865354591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865354591&partnerID=8YFLogxK
U2 - 10.3390/s120810326
DO - 10.3390/s120810326
M3 - Article
C2 - 23112602
AN - SCOPUS:84865354591
SN - 1424-8220
VL - 12
SP - 10326
EP - 10338
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 8
ER -