High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture
Received: 15 December 2017 / Revised: 20 February 2018 / Accepted: 21 March 2018 / Published: 23 March 2018
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Omnidirectional imaging technology has been widely used for scene archiving. It has been a crucial technology in many fields including computer vision, image analysis and virtual reality. It should be noted that the dynamic range of luminance values in a natural scene is
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Omnidirectional imaging technology has been widely used for scene archiving. It has been a crucial technology in many fields including computer vision, image analysis and virtual reality. It should be noted that the dynamic range of luminance values in a natural scene is quite large, and the scenes containing various objects and light sources consist of various spectral power distributions. Therefore, this paper proposes a system for acquiring high dynamic range (HDR) spectral images for capturing omnidirectional scenes. The system is constructed using two programmable high-speed video cameras with specific lenses and a programmable rotating table. Two different types of color filters are mounted on the two-color video cameras for six-band image acquisition. We present several algorithms for HDR image synthesis, lens distortion correction, image registration, and omnidirectional image synthesis. Spectral power distributions of illuminants (color signals) are recovered from the captured six-band images based on the Wiener estimation algorithm. In this paper, we present two types of applications based on our imaging system: time-lapse imaging and gigapixel imaging. The performance of the proposed system is discussed in detail in terms of the system configurations, acquisition time, artifacts, and spectral estimation accuracy. Experimental results in actual scenes demonstrate that the proposed system is feasible and powerful for acquiring HDR spectral scenes through time-lapse or gigapixel omnidirectional imaging approaches. Finally, we apply the captured omnidirectional images to time-lapse spectral Computer Graphics (CG) renderings and spectral-based relighting of an indoor gigapixel image.