A Reconstruction Method for Disparity Image Based on Region Segmentation and RBF Neural Network

摘要

The Reconstruction for disparity image is the key technology for 3D image restoration in stereovision field. However, the data volume of disparity images is so large and the topological structures of disparity images is so complicated that reconstruction for disparity image is very difficult. We had done a lot of works for the sake of constructing a new method to reconstruct disparity image. In this study, a reconstruction method for disparity image based on region segmentation and isomorphic RBF(Radical Basis Function) neural network is presented. First, the disparity image is divided into some regions with adjustable threshold and edge detection. Next, reconstruction based on RBF neural network is carried out in every region, in which process the disparity point clouds are optimized. Then, all the regions are connected and the reconstruction result is obtained. When reconstruction based on RBF is executed in different regions, trainings are carried on with different resolution data according to the complexity of the structures of different regions. Experimental results show that the method proposed in this study can attain reconstruction results of high quality effectively.