Motion estimation and adaptive video reconstruction method based on interpolation

Abstract

The invention discloses a motion estimation and adaptive video reconstruction method based on interpolation. In the time super-resolution video reconstruction process based on compression perception,for a sample learning reconstruction algorithm, the close correlation between the video image reconstruction quality and training samples is considered, and adaptive classification reconstruction is carried out according to the object exercise amount. According to the method, firstly, a coded image is acquired through a pixel continuous equal-length exposure mode, secondly, the motion area is segmented through utilizing a method based on coded image scattering characteristics, initial video images are rapidly acquired through utilizing an interpolation method, the exercise amount of each areais estimated based on the initial images, and lastly, corresponding training parameters are selected according to different motion amount to carry out classification reconstruction. The method is advantaged in that the scene motion information is rapidly acquired before image reconstruction, and the video image reconstruction quality is further improved.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an interpolation-based motion estimation and adaptive temporal super-resolution video reconstruction method. Background technique [0002] The temporal super-resolution video reconstruction technology based on compressed sensing is to obtain encoded observation images by performing single-pixel exposure encoding on pixels, and then use 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 of reconstructed video images is the focus of attention. At present, the algorithm with the best reconstruction effect is the algorithm based on sample learning. This type of algorithm first obtains...

AI Technical Summary

Problems solved by technology

However, how to accurately and quickly segment the regions with different motions from the single-frame coded image needs to be solved first. The traditional method includes segmentation based on the shot feature of the motion region in the coded image and estimation of the motion amount through the preliminary reconstructed sequence images. The former problem It can only segment the moving and static areas, and cannot further distinguish the exact amount of motion in the moving area. The latter is time-consuming and the estimation accuracy of the amount of motion is not enough.

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