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

• Published in: Sensors (Basel, Switzerland) Vol. 12; no. 8; pp. 10326 - 10338
• Main Authors: Huang, Xiaopeng, Netravali, Ravi, Man, Hong, Lawrence, Victor
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01-08-2012; Molecular Diversity Preservation International (MDPI)
• Subjects: Algorithms; Blended; Blending; Computer engineering; Darkness; EO image edge detection; Fourier transforms; High resolution; image pair blending/superimposing; Image Processing, Computer-Assisted - methods; Infrared radiation; Infrared Rays; inverse filter design; IR image transformation; Optical transfer function; Optics; Optics and Photonics - methods; Sensors; Signal Processing, Computer-Assisted; Simulation; theoretical PSF; Thermography; Transformations; Wavelet transforms; United States > US; inverse filter design; EO image edge detection; image pair blending/superimposing; IR image transformation; theoretical PSF
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s120810326

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.