Wavelet Transforms in Parallel Image Processing

摘要

This project consists of two parts: applications of wavelet transforms inmultiscale image processing, and parallel algorithms and architectures. We have studied issues in wavelet-based edge detection: antisymmetry of wavelet filters and their support size with respect to edge localization, and a non-orthogonal four-coefficient wavelet edge detector. Texture segmentation using a modulated Daubechies wavelet has been studied, providing both spatial frequency and orientation selectivity. Object segmentation has been studied by examining silhouettes at multiple resolutions in piecewise linear approximations, and has been explored on LADAR data for use in target recognition. Applications to biomedical image compression, image halftoning and artificial neural network structure have also been investigated. In parallel processing, we have studied embeddings of wavelet transform algorithms as well as other algorithms for 2D and 3D mesh architectures and systolic arrays, and their time complexities have been evaluated. A variety of issues are addressed in utilization of 3D meshes in parallel image processing on 2D and 3D images including: the embedding of 2D images into 3D meshes, 3D shrinking incorporating subfields methodology, 3D connected component labeling, and segmentation in magnetic resonance imaging. Some fundamental issues for parallel reduction and reduction-augmentation operators in 2D and 3D image spaces have been examined.