Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches

LIAN Qiu-sheng; ZHANG Wei

doi:10.3969/j.issn.0372-2112.2012.05.010

您当前的位置：

首页 >

文章列表页 >

Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches

更新时间：2025-07-16

- Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches
- Acta Electronica Sinica Vol. 40, Issue 5, Pages: 920-925(2012)
- 作者机构：
  
  燕山大学信息科学与工程学院,河北,秦皇岛,066004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2012.05.010
  CLC： TN911.73
- Published：2012
- 稿件说明：
移动端阅览
LIAN Qiu-sheng, ZHANG Wei. Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches[J]. Acta Electronica Sinica, 2012, 40(5): 920-925.
DOI：

LIAN Qiu-sheng, ZHANG Wei. Image Super-Resolution Algorithms Based on Sparse Representation of Classified Image Patches[J]. Acta Electronica Sinica, 2012, 40(5): 920-925. DOI： 10.3969/j.issn.0372-2112.2012.05.010.

摘要

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.

Abstract

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.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multimorphology Sparsity Regularized Image Super-Resolution

Image Super-Resolution Reconstruction via Improved Dictionary Learning Based on Coupled Feature Space

Scene Text Image Super-Resolution Reconstruction Based on Perceiving Multi-Domain Character Distance

A Spatiotemporal Fusion Algorithm of Remote Sensing Images Based on Cross-Scale Similarity Prior

Sparse ISAR Imaging Combined with Nearest Neighbor Graph Model

Related Author

SUN Yu-bao

WEI Zhi-hui

XIAO Liang

ZHANG Zhen-rong

詹曙

方琪

杨福猛

Related Institution

Lab of Pattern Recognition and Artificial Intelligence,Institute of Computer Science and Technology, Nanjing University of Science and Technology

Scientific Research Department of Military Training, 60th Research Institute of General Staff Department,Chinese People's Liberation Army

Lab of Pattern Recognition and Artificial IntelligenceInstitute of Computer Science and Technology Nanjing University of Science and TechnologyNanjingJiangsu 210094China

Scientific Research Department of Military Training 60th Research Institute of General Staff DepartmentChinese People's Liberation ArmyNanjingJiangsu 210016China

School of Computer & Information, Hefei University of Technology

⁰