Intelligent Texture Reconstruction of Missing Data in Video Sequences Using Neural Networks

Abstract

The paper provides an intelligent method of texture reconstruction after removal of non-disabled objects or artifacts in video sequences. Data under subtitles, logotypes, damages of information medium or small size objects are referred to as missing data. A novel implementation of separated neural network was used to receive spatial texture estimations in missing data region. Usually several types of textures are located under removed object. A fast wave algorithm was developed for boundary interpolation between different types of texture into a missing data region. Three strategies of wave algorithm for contour optimization were suggested. A fully connected one-level neural network was applied for choice of texture inpainting method (blurring, texture tile, and texture synthesis). The proposed technique was tested for visual reconstruction of missing text regions (subtitles, logotypes) and missing objects with area less 8-12% of frame in animation and movies. In the first case, a simplified decision without stage of boundaries approximation may be applied; in the second case, the reconstruction results are significantly determined by a background complexity and motion in scene.