[1]ZHAO Zhihui,ZHAO Ruizhen,CEN Yigang,et al.Rapid super-resolution image reconstruction based on sparse representation and linear regression[J].CAAI Transactions on Intelligent Systems,2017,12(1):8-14.[doi:10.11992/tis.201603039]
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Rapid super-resolution image reconstruction based on sparse representation and linear regression

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 1 Page number: 8-14 Column: 学术论文—机器感知与模式识别 Public date: 2017-02-25
Title:
Rapid super-resolution image reconstruction based on sparse representation and linear regression
Author(s):
ZHAO Zhihui1; 2;  ZHAO Ruizhen1; 2;  CEN Yigang1; 2;  ZHANG Fengzhen1; 2
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
Keywords:
linear regression; super-resolution; dictionary learning; sparse representation; image reconstruction; feature learning; subspace; neighborhood embedding
CLC:
TP391.41
DOI:
10.11992/tis.201603039
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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