The theory of opponent-sensor image fusion is based on neural circuit models of adaptive contrast enhancement and opponent-color interaction, as developed and previously presented by Waxman, Fay et al. This approach can directly fuse 2, 3, 4, and 5 imaging sensors, e.g., VNIR, SWIR, MWIR, and LWIR for fused night vision. The opponent-sensor images also provide input to a point-and-click fast learning approach for target fingerprinting (pattern learning and salient feature discovery) and subsequent target search. We have recently developed a real-time implementation of multi-sensor image fusion and target learning & search on a single board attached processor for a laptop computer. In this paper we will review our approach to image fusion and target learning, and demonstrate fusion and target detection using an array of VNIR, SWIR and LWIR imagers. We will also show results from night data collections in the field. This opens the way to digital fused night vision goggles, weapon sights and turrets that fuse multiple sensors and learn to find targets designated by the operator.