基于图像块分类稀疏表示的超分辨率重构算法
Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches
查看参考文献16篇
|
文摘
|
目前基于图像块稀疏表示的超分辨率重构算法对所有图像块都用同一字典表示,不能反映不同类型图像块间的差别.针对这一缺点,本文提出基于图像块分类稀疏表示的方法.该方法先利用图像局部特征将图像块分为平滑、边缘和不规则结构三种类型,其中边缘块细分为多个方向.然后利用稀疏表示方法对边缘和不规则结构块分别训练各自对应的低分辨率和高分辨率字典.重构时对平滑块利用简单双三次插值方法,边缘和不规则结构块由其对应的高、低分辨率字典通过正交匹配追踪算法重构.实验结果表明,与单字典稀疏表示算法相比,本文算法对图像边缘部分重构质量明显改善,同时重构速度显著提高. |
|
其他语种文摘
|
At present,super-resolution algorithms based on sparse representation of image patches exploit single dictionary to represent the image patches,which can not reflect the differences of various image patches types.In this paper,a novel method based on sparse representation of classified image patches is proposed to overcome this disadvantage.In this method,image patches are firstly divided into smooth patches,different directional edge patches and irregular structure patches by local features.Then these classified patches are applied into training the corresponding high and low resolution dictionary pairs.During the reconstruction process,simple bicubic interpolation approach is used for smooth patches while edge and irregular structure patches are reconstructed from their corresponding dictionary pairs using orthogonal matching pursuit algorithm.Experiment results show that the proposed algorithm significantly improves the visual quality of the edges and has faster speed compared with other single dictionary methods. |
|
来源
|
电子学报
,2012,40(5):920-925 【核心库】
|
|
DOI
|
10.3969/j.issn.0372-2112.2012.05.010
|
|
关键词
|
超分辨率
;
稀疏表示
;
块分类
;
正交匹配追踪
|
|
地址
|
燕山大学信息科学与工程学院, 河北, 秦皇岛, 066004
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0372-2112 |
|
学科
|
电子技术、通信技术 |
|
基金
|
国家自然科学基金
;
河北省自然科学基金
|
|
文献收藏号
|
CSCD:4544692
|
参考文献 共
16
共1页
|
|
1.
Hou H S. Cubic spline for image interpolation and digital filtering.
IEEE Transaction on Signal Pressing,1978,26(6):508-517
|
CSCD被引
7
次
|
|
|
|
|
2.
Mallet S. Super-Resolution with sparse mixing estimators.
IEEE Transactions on Image Processing,2010,19(11):2889-2900
|
CSCD被引
2
次
|
|
|
|
|
3.
邵文泽. 基于各向异性MRF建模的多帧图像变分超分辨率重建.
电子学报,2009,36(6):1256-1263
|
CSCD被引
9
次
|
|
|
|
|
4.
韩玉斌. 一种视频序列的超分辨率重建算法.
电子学报,2005,33(1):126-130
|
CSCD被引
2
次
|
|
|
|
|
5.
Freeman W T. Example-based super-resolution.
IEEE Computer Graphics and Applications,2002,22(2):56-65
|
CSCD被引
215
次
|
|
|
|
|
6.
Elad M. Example-based regularization deployed to super-resolution reconstruction of a single image.
The Computer Journal,2007,50(4):1-16
|
CSCD被引
8
次
|
|
|
|
|
7.
Yang Jianchao. Image super-resolution via sparse representation.
IEEE Transaction on Image Processing,2010,19(11):2861-2873
|
CSCD被引
84
次
|
|
|
|
|
8.
Yang Jianchao. Image super-resolution as sparse representation of raw image patches.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2008:1-8
|
CSCD被引
1
次
|
|
|
|
|
9.
Zeyde R. On single image scale-up using sparse-representations.
Proceedings of the 7th International Conference on Curves and Surfaces,2010
|
CSCD被引
2
次
|
|
|
|
|
10.
Aharon M. The K-SVD:an algorithm for designing of overcomplete dictionaries for sparse representation.
IEEE Transaction on Signal Processing,2006,54(11):4311-4322
|
CSCD被引
871
次
|
|
|
|
|
11.
Rubinstein R.
Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit,2008
|
CSCD被引
13
次
|
|
|
|
|
12.
He Xiaofei. Locality preserving projections.
Advances in Neural Information Processing Systems,2003:153-160
|
CSCD被引
34
次
|
|
|
|
|
13.
Pun T. Entropic thresholding, a new approach.
Computer Graphics and Image Processing,1981,16(3):210-239
|
CSCD被引
54
次
|
|
|
|
|
14.
Feng Xiaoguang. Multiscale principal components analysis for image local orientation estimation.
Proceedings of the 36th Asilomar Conference on Signals, Systems and Computers,2002:478-482
|
CSCD被引
3
次
|
|
|
|
|
15.
Glasner D. Super-Resolution from a Single Image.
Proceedings of the 12th IEEE International Conference on Computer Vision,2009:349-356
|
CSCD被引
10
次
|
|
|
|
|
16.
Irani M. Improving resolution by image registration.
Graph Models Image Process,1991,53(3):231-239
|
CSCD被引
208
次
|
|
|
|
|
了解气象卫星更多信息，请访问