Super Resolution Reconstruction Algorithm Combined Low Rank with Sparse Representation

doi:10.7652/xjtuxb201807003

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Super Resolution Reconstruction Algorithm Combined Low Rank with Sparse Representation

更新时间：2025-07-09

- Super Resolution Reconstruction Algorithm Combined Low Rank with Sparse Representation
- Vol. 52, Issue 7, Pages: 18-24(2018)
- 作者机构：
  
  中原工学院理学院,郑州,451191
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201807003
  CLC： TP391.41
- Online First：10 July 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
Super Resolution Reconstruction Algorithm Combined Low Rank with Sparse Representation[J]. 2018, 52(7): 18-24.
DOI：

Super Resolution Reconstruction Algorithm Combined Low Rank with Sparse Representation[J]. 2018, 52(7): 18-24. DOI： 10.7652/xjtuxb201807003.

摘要

针对现有图像超分辨率重建算法收敛速度慢、易受噪声影响的问题

结合低秩矩阵恢复与稀疏重建理论

提出了一种新的单幅图像超分辨率重建算法。对于待重建的退化图像

首先进行低秩恢复

得到含有原始图像大部分信息的低秩部分和主要由噪声组成的稀疏部分

然后对低秩部分利用学习的高低分辨率字典对进行稀疏重建。实验结果表明:本文算法对噪声鲁棒

运行速度快

图像视觉效果更佳; 相比基于稀疏表示的统计预测模型(SPBSR)

本文算法的峰值信噪比指标平均提高了4 dB。

Abstract

Traditional super-resolution(SR)reconstruction algorithm converges slowly

and is easily vulnerable to noise. We propose a novel single image SR reconstruction algorithm

which combines low rank matrix recovery with the sparse reconstruction theory. For a degraded image

the low rank part and the sparse part are obtained by the low rank matrix recovery theory firstly. The low rank part contains almost all information of the original image

and the sparse part is composed of noise. Then the sparse reconstruction theory is used to get the final reconstructed image on the low rank part with low and high resolution dictionary pair. Experimental results demonstrate that the proposed algorithm is robust to noise

gains clear visual appearance and high efficiency

and achieves a desirable improvement of 4 dB averagely compared with SPBSR.

关键词

Keywords

references

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