Temporal super-resolution based on pixel stream and featured prior model for motion blurred single

摘要

Single-video-based temporal super-resolution reconstruction (SRR) can get rid of constraints imposed on multiple-video-based temporal SRR, including device scarcity, temporal synchronization, complicated registration, and high cost. But frame interpolation in single-video-based temporal SRR cannot deblur well, and the selection or determination related to interpolation function has a lack of evidence, which reduces the fidelity. Additionally, the subsequent spatial deblurring is suboptimal for the motion blur in video because the formation of the motion blur is different from that of the spatial blur. This paper proposes a temporal SRR based on pixel stream and featured prior model to increase the frame rate and the definition of motion blurred single video. The proposed temporal SRR views the single video as a bundle of pixel streams and implements maximum-a-posteriori-based pixel stream SRR stream-by-stream without the selection or determination related to interpolation function. What is more, a featured temporal prior about the pixel stream is proposed and introduced into the pixel stream SRR, which can well remove the motion blur. Experimental results show the proposed temporal SRR is effective. (C) 2016 SPIE and IS&T