Low-level vision based super-resolution image reconstruction

doi:10.3778/j.issn.1002-8331.2009.29.049

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (29): 164-167.DOI: 10.3778/j.issn.1002-8331.2009.29.049

• 图形、图像、模式识别 • Previous Articles Next Articles

Low-level vision based super-resolution image reconstruction

GUO Hai-xia¹，LIU Hai-ou²，GUO Hai-long³，XIE Kai⁴

1.Harbin Finance College，Harbin 150030，China
2.Deqiang Business College，Harbin University of Commerce，Harbin 150028，China
3.Harbin University，Harbin 150086，China
4.College of Information & Mechanical Engineering，Beijing Institute of Graphic Communication，Beijing 102600，China

Received:2008-04-07 Revised:2008-07-14 Online:2009-10-11 Published:2009-10-11
Contact: GUO Hai-xia

基于低层次计算机视觉的超分辨率图像重建

郭海霞¹，刘海欧²，郭海龙³，解凯⁴

1.哈尔滨金融高等专科学校，哈尔滨 150030
2.哈尔滨商业大学德强商务学院，哈尔滨 150028
3.哈尔滨学院，哈尔滨 150086
4.北京印刷学院信息与机电学院，北京 102600

通讯作者: 郭海霞

Abstract

Abstract: In the process of super-resolution image reconstruction，corner detection and interpolation are two key technologies.In this paper，two approaches are proposed respectively.Firstly，an improved corner detection approach based on SUSAN principle for feature detection is proposed.It has variable thresholds for the allowed variation in brightness within the USAN.It makes corner well-distributed.Secondly，a new adaptive interpolation approach based on circular-area is proposed.The approach can adaptively select the interpolation method based on gray feature around the interpolated point.In this method，linear interpolation，nearest-neighbor interpolation and median interpolation are merged.Experiments confirm that these algorithms are superior to others.

Key words: corner detection, super-resolution, image reconstruction, SUSAN, adaptive interpolation

摘要： 在基于低层次计算机视觉的超分辨率图像重建过程中，角点检测和插值是两个关键的技术。首先在SUSAN角点检测算法的基础上提出了改进算法，改进后的算法根据图块对比度的不同，在确定位于不同图块中的像素的USAN面积时采用了可变灰度阈值，可变灰度阈值的采用，使得检测出的角点分布更加均匀，而角点分布均匀则使得图像配准更加精确，有利于后期的重建工作。其次，提出了一种适合于超分辨率图像重建的插值算法：基于圆区域的自适应插值算法。该算法可以根据待插值点周围的灰度特征自适应决定插值策略，将线性插值、最邻近插值和中值插值法有机地结合在一起。大量的仿真实验证明了提出算法具有运算量小、图像重建后的效果出重，易于实现。

关键词: 角点检测, 超分辨率, 图像重建, SUSAN角点检测算法, 自适应插值

CLC Number:

TP391.41

GUO Hai-xia¹，LIU Hai-ou²，GUO Hai-long³，XIE Kai⁴. Low-level vision based super-resolution image reconstruction[J]. Computer Engineering and Applications, 2009, 45(29): 164-167.

郭海霞¹，刘海欧²，郭海龙³，解凯⁴. 基于低层次计算机视觉的超分辨率图像重建[J]. 计算机工程与应用, 2009, 45(29): 164-167.

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Low-level vision based super-resolution image reconstruction

基于低层次计算机视觉的超分辨率图像重建

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