Super-resolution image reconstruction via patch haar wavelet feature extraction combined with sparse coding

2015 IEEE International Conference on Information and Automation Pub Date : 2015-10-01 DOI:10.1109/ICINFA.2015.7279388

Xuan Zhu, Benyuan Li, Jiyao Tao, Bo Jiang

引用次数: 5

Abstract

This paper presents a new approach to single-image super-resolution reconstruction, based on patch haar wavelet feature extraction combined with sparse coding. The training sample set is constructed by image patches haar wavelet transform to extract the horizontal, vertical and diagonal high frequency component composition column feature vector. Then, we train a pair of learning dictionaries which have good adaptive ability by using joint training method. Learning dictionaries combined with sparse coding theory to realize the image super-resolution reconstruction. As the experiment results show, the new method has good performs for recovering the lost high frequency information, and has good robustness.

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基于patch haar小波特征提取和稀疏编码的超分辨率图像重建

提出了一种基于patch haar小波特征提取与稀疏编码相结合的单幅图像超分辨率重建方法。通过图像小波变换构造训练样本集，提取水平、垂直、对角高频成分组成列特征向量。然后，采用联合训练方法训练一对具有良好自适应能力的学习字典。学习字典结合稀疏编码理论实现图像超分辨率重建。实验结果表明，该方法对丢失的高频信息有较好的恢复效果，并且具有较好的鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Conference on Information and Automation

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