Video stabilization method based on empirical mode decomposition and multiple evaluation criteria

Abstract

The invention discloses a video stabilization method based on empirical mode decomposition and multiple evaluation criteria, relates to the technical field of video repairing and image processing, and provides a video stabilization technology capable of removing unstable and jitter effects from a video. The method comprises the steps of extracting feature points from a reference frame and a current frame with a Sift algorithm, matching the feature points, obtaining a global motion parameter with an RANSAC (Random Sampling Consensus) algorithm, determining jitter parameters based on the empirical mode decomposition and the multiple evaluation criteria (position errors of the global motion parameter and feature point pairs) to realize motion compensation for the current frame, repairing a video frame after the motion compensation with an image texture synthesis algorithm, and obtaining the stable and complete output video. Objective experimental results prove that the method has better comprehensive advantages in the signal to noise ratio and time consuming.

Description

technical field [0001] The invention relates to the technical fields of video restoration and image processing, in particular to a video de-jittering method based on empirical mode decomposition and multiple evaluation criteria, which is a video de-jittering technology capable of removing unstable and jittering effects in videos. Background technique [0002] In an actual photography system, the video obtained by a mobile platform such as a vehicle, a handheld or an aircraft includes not only the active motion of the imaging system, but also the random motion of the mobile platform. The unstable video generated by the random motion will make people feel tired and make it difficult to extract useful information. Therefore, how to convert unstable video into stable video is of great significance. [0003] The current methods are divided into pixel method, block matching method, phase correlation method and feature matching method. The pixel method uses the relationship betwe...

AI Technical Summary

Problems solved by technology

The pixel method uses the relationship between pixel gray values ​​for motion estimation, but it is sensitive to noise and requires rich image information; the block matching method uses the pixels in the block as a whole to perform motion estimation, so it is more accurate than the pixel method. Robust, but the accuracy and computational complexity of the algorithm are greatly affected by the number, size, search range and search strategy of the block; the phase correlation method estimates the direction and speed of motion by calculating the cross-power spectrum of adjacent frames, and its Strong noise resistance, but computationally complex and susceptible to local motion interference; feature matching method is based on human visual characteristics, by extracting and matching the features of adjacent frames for motion estimation, compared with other algorithms, it is closer to The human visual system processes motion information, but the result is limited by the robustness and accuracy of feature extraction and matching

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