中山大学学报自然科学版 ›› 2011, Vol. 50 ›› Issue (4): 11-16.

• 研究论文 • 上一篇    下一篇

基于几何分类的自适应图像插值算法

梁 云1,2,4,5,朱为鹏2,李 峥2,3

  

  1. (1. 华南农业大学信息学院, 广东 广州 510642;2.中山大学信息科学与技术学院∥数字家庭教育部重点实验室,广东 广州 510006;3.广东工业大学计算机学院, 广东 广州 510090;4.深圳市数字生活网络与内容服务重点实验室,广东 深圳 518057;5.中山大学深圳研究院,广东 深圳 518057)
  • 收稿日期:2010-06-11 修回日期:1900-01-01 出版日期:2011-07-25 发布日期:2011-07-25

A New Adaptive Image Interpolation Method based on Geometric Classification

LIANG Yun1,2,4,5, ZHU Weipeng2, LI Zheng2,3   

  1. (1.College of informatics, South China Agricultural University, Guangzhou 510642, China;2. School of Information Science & Technology∥Key Laboratory of Digital Life, Ministry of Education, Sun Yatsen University, Guangzhou 510006, China;3. Faculty of Computer, Guangdong University of Technology, Guangzhou 510090, China;4. Shenzhen Key Laboratory of Digital Living Network and Content Service,Shenzhen 518057,China;5. Research Institute of Sun Yatsen University in Shenzhen,Shenzhen 518057,China)
  • Received:2010-06-11 Revised:1900-01-01 Online:2011-07-25 Published:2011-07-25

摘要: 图像插值是放大低分辨图像以适应目标屏幕的有效方法。低分辨率图像边缘特征保持越好,则插值图像的效果越好。根据低分辨图像的边缘分布特征对插值单元几何分类,提出了一种自适应图像插值算法。首先根据高分辨率图像中像素点的相对位置构造矩形插值单元和菱形插值单元,所有未知像素点位于矩形插值单元或菱形插值单元;然后从8个方向,特别是斜对角方向计算插值单元的图像边缘,并将边缘作为割线,根据割线对插值单元进行几何分类,可分为16类;最后根据未知像素点所属的插值单元分类计算未知像素值。实验证明,该算法比现有多种插值算法能够更好的保持图像边缘的尖锐特征。

关键词: 图像插值, 几何分类, 边缘保持, 插值单元

Abstract: Image interpolation is an effective method to magnify image with low resolution to adapt to the target screen. Better the preservation of image edges brings much better magnifying result. A new adaptive image interpolation approach based on geometric classification of interpolate units, which is formed by edges of image with low resolution, is proposed. Firstly, we construct rectangle interpolate units and rhombic interpolate units by the relationship of pixels in image with high resolution. All pixels of the superresolution image lie in the two kinds of interpolate units. Then we analysis possible edges of original images especially along the oblique line and treat edges as the secants of interpolate units to classify every interpolate unit. There are total 16 kinds of interpolate units. At last, we calculate unknown pixels based on the classified interpolate units. Experiments show that our new adaptive image interpolation scheme based on geometric classification can preserve edges of interpolated images better than many present methods.

Key words: image interpolation, geometric classification, edge preservation, interpolate unit

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