Abstract
The industry practice for night video surveillance is to use auxiliary near-infrared (NIR) LEDs, usually centered at 850nm or 940nm, for scene illumination. NIR LEDs are used to save power consumption while hiding the surveillance coverage area from naked human eyes. The captured images are almost monochromatic, and visual color and texture tend to disappear, which hinders human and machine perception. A few existing studies have tried to convert such NIR images to RGB images through deep learning, which can not provide satisfying results, nor generalize well beyond the training dataset. In this paper, we aim to break the fundamental restrictions on reliable NIR-to-RGB (NIR2RGB) translation by examining the imaging mechanism of single-chip silicon-based RGB cameras under NIR illuminations, and propose to retrieve the optimal LED multiplexing via deep learning. Experimental results show that this translation task can be significantly improved by properly multiplexing NIR LEDs close to the visible spectral range than using 850nm and 940nm LEDs.
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