Image Super-Resolution Reconstruction Based on Improved K-SVD Dictionary-Learning

SHI Jun; WANG Xiao-hua

doi:10.3969/j.issn.0372-2112.2013.05.026

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Image Super-Resolution Reconstruction Based on Improved K-SVD Dictionary-Learning

更新时间：2025-07-16

- Image Super-Resolution Reconstruction Based on Improved K-SVD Dictionary-Learning
- Acta Electronica Sinica Vol. 41, Issue 5, Pages: 997-1000(2013)
- 作者机构：
  
  北京理工大学信息与电子学院,北京,100081
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.05.026
  CLC： TP391.41
- Published：2013
- 稿件说明：
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SHI Jun, WANG Xiao-hua. Image Super-Resolution Reconstruction Based on Improved K-SVD Dictionary-Learning[J]. Acta Electronica Sinica, 2013, 41(5): 997-1000.
DOI：

SHI Jun, WANG Xiao-hua. Image Super-Resolution Reconstruction Based on Improved K-SVD Dictionary-Learning[J]. Acta Electronica Sinica, 2013, 41(5): 997-1000. DOI： 10.3969/j.issn.0372-2112.2013.05.026.

摘要

针对已有算法中字典训练的时间消耗巨大的问题

提出了一种改进的基于字典学习的超分辨率图像重构算法.本文将K-SVD字典算法和高低分辨率联合生成的思想结合起来

形成新的字典训练方法

并将由该算法生成的高低分辨率字典应用于基于稀疏表示的超分辨率重构.重构仿真实验证明算法不仅有效降低了字典训练所消耗的时间

而且能够改善重构高分辨图像的质量.

Abstract

An improved super-resolution image reconstruction algorithm based on dictionary-learning is studied in order to solve the problem that the dictionary training process is time-consuming in the existing algorithms.The K-SVD dictionary algorithm is combined with the idea that the high and low resolution dictionaries can be co-generated.Then the high and low resolution dictionaries generated are used to the super-resolution reconstruction algorithm via sparse representation.Experiment results show that the algorithm can not only reduce the time of the dictionary training effectively

and also improve the quality of the reconstruction of high-resolution images.

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Sparse Dictionary Learning Based on Optimization of Surrogate Function

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

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