Automatic hierarchical background virtualization method for monocular vision image based on depth information extraction

Abstract

Due to the influence of illumination, noise, distortion and other factors on monocular vision images, the image quality is reduced, the difficulty of image information extraction is high, and there are often errors and uncertainties in background segmentation, which affect the effect of monocular vision image background virtualization. Therefore, a new depth information extraction monocular vision image automatic hierarchical background virtualization method is studied to improve the effect of background virtualization. The depth information map is extracted by anisotropic thermal diffusion equation. The morphology is used to fill the tiny holes in the depth information map, and its smoothing process is used to determine the image depth range, automatically layer the depth information map, and obtain the foreground layer and background layer. The background layer is virtualized by Gaussian blur operation. Pyramid image fusion method is used to fuse the foreground layer and the blurred background layer to complete the background virtualization of monocular vision image. Experimental results have shown that this method can effectively improve the clarity of depth information map edges, preserve a large amount of image edge information, and have high structural similarity, with an average value of 0.96. The efficiency is high, and the background virtualization time is only 15 ms.