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This paper presents a dual camera system for high spatiotemporal resolution(HSTR) video acquisition, where one camera shoots a video with high spatialresolution and low frame rate (HSR-LFR) and another one captures a low spatialresolution and high frame rate (LSR-HFR) video. Our main goal is to combinevideos from LSR-HFR and HSR-LFR cameras to create an HSTR video. We propose anend-to-end learning framework, AWnet, mainly consisting of a FlowNet and aFusionNet that learn an adaptive weighting function in pixel domain to combineinputs in a frame recurrent fashion. To improve the reconstruction quality forcameras used in reality, we also introduce noise regularization under the sameframework. Our method has demonstrated noticeable performance gains in terms ofboth objective PSNR measurement in simulation with different publicly availablevideo and light-field datasets and subjective evaluation with real datacaptured by dual iPhone 7 and Grasshopper3 cameras. Ablation studies arefurther conducted to investigate and explore various aspects (such as referencestructure, camera parallax, exposure time, etc) of our system to fullyunderstand its capability for potential applications.

A Dual Camera System for High Spatiotemporal Resolution Video Acquisition