Enhanced Seamless Video Fusion: A Convolutional Pyramid-Based 3D Integration Algorithm

Abstract

Video fusion aims to synthesize video footage from different sources into a unified, coherent output. It plays a key role in areas such as video editing and special effects production. The challenge is to ensure the quality and naturalness of synthetic video, especially when dealing with footage of different sources and qualities. Researchers continue to strive to optimize algorithms to adapt to a variety of complex application scenarios and improve the effectiveness and applicability of video fusion. We introduce an algorithm based on a convolution pyramid and propose a 3D video fusion algorithm that looks for the potential function closest to the gradient field in the least square sense. The 3D Poisson equation is solved to realize seamless video editing. This algorithm uses a multi-scale method and wavelet transform to approximate linear time. Through numerical optimization, a small core is designed to deal with large target filters, and multi-scale transformation analysis and synthesis are realized. In terms of seamless video fusion, it shows better performance than existing algorithms. Compared with editing multiple 2D images into video after Poisson fusion, the video quality produced by this method is very close, and the computing speed of the video fusion is improved to a certain extent.