An Adaptive Video Reconstruction Method Based on Signal Correlation

Abstract

The invention discloses an adaptive video reconstruction method based on signal correlation. The adaptive video reconstruction method is characterized in that during a high time resolution video reconstruction process based on compressed sensing, considering the situation that the velocity of movement for each moving object in a video image is not consistent, aiming at matching, tracking and other reconstruction methods based on a dictionary (a sparse domain), firstly during the process of constructing dictionaries, dividing a training sample into a plurality of sample sets according to difference of the amount of movement during the process of constructing the dictionaries, and respectively training the sample sets so as to obtain dictionaries corresponding to different amount of movement; secondly, during the video reconstruction step, performing blocking reconstruction without superposition on the observed images requiring reconstruction, then calculating the correlation coefficients between each frame of image blocks, and determining the local image amount of movement according to the magnitude of the correlation coefficients; and at last, according to the magnitude of the amount of movement, performing targeted selection of a dictionary for image reconstruction. The adaptive video reconstruction method based on signal correlation can distinguish the amount of movement for each object in the video during the process of reconstructing video signals, and can perform targeted image reconstruction according to the movement information, thus improving the video reconstruction effect and reducing the reconstruction time at the same time.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an adaptive high-time-resolution video reconstruction method based on signal correlation. Background technique [0002] The high-time-resolution video reconstruction technology based on compressed sensing obtains encoded observation images by performing single-pixel exposure encoding on pixels, and then uses reconstruction algorithms to reconstruct the observation images to obtain a series of video sequence images, that is, to obtain three-dimensional images from two-dimensional images. Video temporal resolution extension technology. Since compressed sensing is to restore and reconstruct the sampling signal lower than the Nyquist sampling rate, the accuracy and speed of the reconstructed signal are the focus of attention. Reconstruction algorithms can generally be divided into l-based 1 Norm minimization method, iterative threshold method, matching pursuit method, convex regular...

AI Technical Summary

Problems solved by technology

At the same time, in most cases, people are more concerned about the motion area in the video, so if the entire area of ​​the image is reconstructed in the same way, it will not only waste time, but also cannot obtain the motion information of the object

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