Video dynamic super-resolution processing method and system

Abstract

The invention relates to a video dynamic super-resolution processing method and system. The processing method comprises the steps of obtaining a motion vector of any pixel in a current to-be-processed frame according to motion estimation and calculating reliability degree of the motion vector; extracting the corresponding pixel of the pixel in a former high-resolution frame according to the motion vector of the pixel and carrying out Kalman filtering processing according to the corresponding pixel, thereby obtaining a first processing result; carrying out interpolation processing on the to-be-processed frame to obtain a second processing result; and carrying out weight fusion on the first processing result and the second processing result through utilization of the reliability degree, thereby obtaining a super-resolution frame after fusion. According to the method and the system, the problems of protecting edge information well and improving image edge sawteeth are realized; more image details are restored effectively on the condition of not increasing excessive extra time cost; moreover, relatively high robustness is achieved; and disorder of a super-resolution result or motion blurring resulting from inaccurate motion estimation is avoided.

Description

technical field [0001] The invention belongs to video processing technology, in particular to video super-resolution (Super-resolution, SR) technology, in particular to a video dynamic super-resolution processing method and system. Background technique [0002] With the continuous improvement of the technology of the TV panel industry, the resolution of the display device increases rapidly, but the resolution of the video content does not increase rapidly accordingly. For example, ultra-high-definition TVs are becoming more and more common in the market, but due to the limitation of image acquisition equipment, the resolution of a large number of video sources is still standard-definition or high-definition, which greatly reduces the visual quality of the display. Therefore, it is very important to study the super-resolution (SR) algorithm that uses low-resolution image information to reconstruct high-resolution images. [0003] According to the different information forms ...

AI Technical Summary

Problems solved by technology

This method uses adjacent pixel information at the same position in adjacent frames to synthesize high-frequency information to generate a high-resolution image with sharp edges and rich details, but this method is only suitable for video images with a large number of similar structures , and the synthetic information available is limited

There is also a dynamic super-resolution method using Kalman filtering, which obtains corresponding pixel information through motion compensation in adjacent frames to obtain a more reliable super-resolution image, but this type of method is often restricted by the accuracy of motion estimation , the robustness of the algorithm is not very good

All in all, traditional dynamic super-resolution algorithms generally have the problem of difficult balance between complexity and reconstruction effect, and dynamic super-resolution algorithms using inter-frame motion information generally require motion vectors with higher sub-pixel precision, which cannot be achieved by motion estimation algorithms. Estimates of accurate or complex motion videos tend to produce disordered results

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