半监督P-N学习互信息熵图像稳定

摘要

本文提出一种半监督 P-N 学习互信息熵图像稳定算法，构建一种图像稳定-学习框架(Image Stabilization-Learning, ISL)。在互信息熵图像稳定基础上，引入一种半监督P-N学习机制，对运动数据进行推理训练，并通过对训练数据分类；构建分类训练模型，对运动参数进行预测；在此基础之上，通过评估模型更新运动估计参数，实现对抖动视频序列进行在线补偿。本文经采用互信息熵稳定与半监督P-N学习互信息熵图像算法对抖动视频序列测试对比，运动估计稳定失误率降低至1%，进一步提升视频序列高频去抖动能力。%For dither video sequences in the tracking, detecting, combating situations, the observed target blurred, which is not conducive to the dynamics of the tracked target identification. This paper presents a semi-supervised learning mutual information entropy PN image stabilization algorithm to enhance the ability to jitter frequency video sequences, and reduce motion estimation error rate. PN semi-supervised learning of mutual information image stabilization is based on mutual information entropy theory for image motion estimation, on this basis, build parameters and jitter parameters is observed for motion compensation loop structure. Through the motion compensation parameters, semi-supervised PN learning are observed and performed. Mutual information entropy key parameters are corrected to ensure motion-compensated inter-frame parameter estimates and the actual amount of image jitter error is minimized and the output stability. By using mutual information entropy P-N learning semi-supervised learning algorithm for mutual information entropy image library jitter test comparison, motion estimation error rate reduced to 1%, to further enhance the ability of video sequences de-jitter frequency.