动态视频多帧连续图像形变特征重构方法研究

Research on Deformation Feature Reconstruction of Multi Frame Continuous Images in Dynamic Video

为了提高图像的重构效果,提出一种动态视频多帧连续图像形变特征重构方法。首先采用帧差法对连续帧进行差分运算,将RGB图像转化为灰度图像后,将灰度差的绝对值与阈值采取比较,进而判定图像帧是背景点还是运动目标。然后对当前帧图像的RGB三个通道做差分与加权求和处理,采用卷积神经网络对视频的显著性进行检测。最后引入交替方向乘子算法解决低秩矩阵的极小化问题,通过辅助变量的引入,重构模型对应的增广拉格朗日函数形式,将重构问题转换成对低秩矩阵、特征信号和辅助变量的求解问题。实验结果表明,采用所提方法图像融合的性能评价指标较好,图像重构效果明显优于其它方法,视觉效果和图像的细节性能均有明显的改善。

In order to improve the effect of image reconstruction, a deformation feature reconstruction method of dynamic video multi-frame continuous image is proposed. Firstly, the frame difference method is used to perform the difference operation on the continuous frames. After the RGB image is transformed into a gray image, the absolute value of the gray difference is compared with the threshold to determine whether the image frame is a background point or a moving target. Then, the difference and weighted summation of the three RGB channels of the current frame image are processed, and the significance of the video is detected by convolutional neural network. Finally, the alternating direction multiplier algorithm is introduced to solve the minimization problem of the low-rank matrix. Through the introduction of auxiliary variables, the augmented Lagrange function corresponding to the model is reconstructed, and the reconstruction problem is transformed into the solution of the low-rank matrix, characteristic signal, and auxiliary variables. The experimental results show that the performance evaluation index of image fusion using this method is better, the image reconstruction effect is obviously better than other methods, and the visual effect and image detail performance are significantly improved.