Local Feature Filtering Method for Dynamic Multiframe Video Sequence Images

To improve the quality of local feature filtering for dynamic multiframe video sequence images, this study is aimed at designing an improved nontexture class noise filtering algorithm based on noise construction denoising algorithm and gray histogram of pixel points, and then designs a texture noise denoising algorithm based on texture smoothing processing and circular gradient values. The two algorithms are combined to propose a comprehensive filtering and denoising algorithm for horizontal dynamic video images. The experimental test results show that the normalized correlation coefficient, mutual information quantity, peak signal-to-noise ratio, and information entropy of the integrated filter denoising algorithm are 0.950, 0.935, 0.816, and 0.933 after convergence of the training effect, which are significantly higher than those of the commonly used median denoising algorithm and Kalman denoising algorithm. However, the computational time consumption of the proposed integrated filtering and denoising algorithm is higher than that of the comparison algorithms. The experimental results show that the integrated filtering algorithm for dynamic video images designed in this study can achieve better filtering and image reconstruction results in application scenarios with lower requirements for the timeliness of processing results.