Improvement of 3D Image Reconstruction Algorithm under Shape-based Visual Communication Constraints
Due to the neglect of the visual communication requirement of image authenticity as the criterion,the related methods with shape-based reconstruction have poor results.Therefore,the 3D image reconstruction algorithm is improved from visual constraints.This paper segments and filters the collected target point cloud data,and constructs a dataset.The local spatial difference algorithm is used to solve for each pixel value,and shape-based constraint pixel value difference coefficients are ob-tained to fill the pixels,which gives visual communication constraints to point cloud data.The k-means clustering algorithm based on improved quantum particle swarm and the type-2 entropy fuzzy C-means clustering algorithm based on digital features are used as the basic algorithms.By clustering the data of the target area and background area,an actual target coordinate sys-tem is transformed,and the reconstructed image coordinate system is reconstructed to achieve 3D image reconstruction at dif-ferent scales and angles.The results show that the reconstructed images using the proposed method are more similar to the ac-tual information of the target building,meet the visual communication needs of the images,and the peak signal-to-noise ratio is always above 85 dB,and the mean square error is below 70 pixel.The reconstruction effect is significantly superior.
visual communication constraint3D imageconstrain endowmentclustering algorithmcoordinate system trans-formationimage reconstruction
万方数据
维普
