邵海梅, 李飞鹏, 秦前清. 基于五株采样提升算法的图像二叉分解与重构[J]. 武汉大学学报 ( 信息科学版), 2004, 29(7): 628-631,634.
引用本文: 邵海梅, 李飞鹏, 秦前清. 基于五株采样提升算法的图像二叉分解与重构[J]. 武汉大学学报 ( 信息科学版), 2004, 29(7): 628-631,634.
SHAO Haimei, LI Feipeng, QIN Qianqing. Bi-graph Image Decomposition Based on Quincunx Sampling Lifting Scheme[J]. Geomatics and Information Science of Wuhan University, 2004, 29(7): 628-631,634.
Citation: SHAO Haimei, LI Feipeng, QIN Qianqing. Bi-graph Image Decomposition Based on Quincunx Sampling Lifting Scheme[J]. Geomatics and Information Science of Wuhan University, 2004, 29(7): 628-631,634.

基于五株采样提升算法的图像二叉分解与重构

Bi-graph Image Decomposition Based on Quincunx Sampling Lifting Scheme

  • 摘要: 提出了一种基于五株采样的提升算法,实现了一分为二的分解与重构。通过此算法可以构造非线性的形态小波变换,保持图像的几何信息。

     

    Abstract: This paper presents a bi-graph image decomposition based on quincunx sampling lifting scheme, which decomposes an original image to a lower-resolution one and a different one between the original image and the lower-resolution image. The proposed scheme is of low-complexity and need not allocate additional memory.This paper introduces some simple examples, such as linear mean-lifting, nonlinear max-lifting and min-lifting. Mean-lifting is good at erasing redundant data, and max-lifting or min-lifting can effectively preserve important geometric information.

     

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