Edge detection, spatial smoothing, and image reconstruction with partially observed multivariate data

• 成果类型: Article
• 署名作者: Dass, SC; Nair, VN
• 署名单位: Michigan State University; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/01621450338861911
• 发表日期: 2003
• 页码: 77-89
• 关键词: statistical-analysis; interpolation

摘要

Situations with incomplete multivariate spatial data on a lattice are considered. The goal is to impute the missing data in the presence of edges or boundaries and recover the image. Two methods based on Bayesian hierarchical models that iterate between edge detection and spatial smoothing to impute the missing data within identified homogeneous regions are examined. Their performance is compared with another method that imputes the missing values using edge-preserving spatial smoothers with locally varying weights. The performances of the three methods are compared on artificial and real datasets. It is seen that information from the multivariate data is critical in recovering the images. An application with color images where only one of three primary colors (red, green, or blue) is observed at each pixel is used to illustrate the results.