| 基于稀疏表示与线性回归的图像快速超分辨率重建 |
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| 引用本文： | 赵志辉,,赵瑞珍,,岑翼刚,,张凤珍,.基于稀疏表示与线性回归的图像快速超分辨率重建[J].智能系统学报,2017,12(1):8-14. |
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| 作者姓名： | 赵志辉 赵瑞珍 岑翼刚 张凤珍 |
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| 作者单位： | 1. 北京交通大学 信息科学研究所, 北京 100044;2. 北京交通大学 现代信息科学与网络技术北京市重点实验室, 北京 100044 |
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| 摘 要： | 单幅图像超分辨率的目的是从一幅低分辨率的图像来重构出高分辨率的图像。基于稀疏表示和邻域嵌入的超分辨率图像重建方法使得重建图像质量有了极大的改善。但这些方法还很难应用到实际中，因为其重建图像的速度太慢或者需要调节复杂的参数。目前大多数的方法在图像重建的速度和质量两个方面很难有一个好的权衡。鉴于以上问题提出了一种基于线性回归的快速图像超分辨率重建算法，将稀疏表示和回归的方法有效地结合在一起。通过稀疏表示训练的字典，用一种新的方式将整个数据集划分为多个子空间，然后在每一类子空间中独立地学习高低分辨率图像之间的映射关系，最后通过选择相应的投影矩阵来重建出高分辨图像。实验结果表明，相比于其他方法，本文提出的算法无论在图像重建速度还是重建质量方面都取得了更好的超分辨率重建效果。
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| 关 键 词： | 线性回归 超分辨率 字典训练 稀疏表示 图像重建 特征训练 子空间 邻域嵌入 |
Rapid super-resolution image reconstruction based on sparse representation and linear regression |
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| ZHAO Zhihui,,ZHAO Ruizhen,,CEN Yigang,,ZHANG Fengzhen,.Rapid super-resolution image reconstruction based on sparse representation and linear regression[J].CAAL Transactions on Intelligent Systems,2017,12(1):8-14. |
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| Authors: | ZHAO Zhihui ZHAO Ruizhen CEN Yigang ZHANG Fengzhen |
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| Affiliation: | 1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China |
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| Abstract: | Single-image super-resolution aims at reconstructing a high-resolution image from a single low-resolution image. Recent methods relying on both neighborhood embedding and sparse coding have led to significant quality improvements. However, the application of these approaches is still practically difficult because they are either too slow or demand tedious parameter tweaks. In most of these methods, the speed and quality of image reconstruction are the two aspects that cannot be balanced easily. With regard to the abovementioned problems, this research proposed a rapid image super-resolution reconstruction algorithm based on linear regression, which effectively combined the sparse representation with the regression method. First, a dictionary was trained using the K-SVD algorithm based on training samples. Subsequently, the entire dataset was divided into a number of subspaces according to the atoms in the dictionary. Moreover, the mapping from low-to-high-resolution images can be independently obtained for each subspace. Finally, the high-resolution image was reconstructed by selecting the corresponding projection matrix. Experimental results demonstrate that both the image reconstruction quality and the speed of the proposed algorithm performed better than other widely used methods. |
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| Keywords: | linear regression super-resolution dictionary learning sparse representation image reconstruction feature learning subspace neighborhood embedding |
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