Image super-resolution reconstruction via improved dictionary learning based on coupled feature space

被引：0

作者：

Zhan S. ^{[1
]}

Fang Q. ^{[1
]}

Yang F.-M. ^{[2
]}

Chang L.-L. ^{[1
]}

Yan T. ^{[1
]}

机构：

[1] School of Computer & Information, Hefei University of Technology, Hefei, 230009, Anhui

[2] School of Electronic Information Engineering, Sanjiang University, Nanjing, 210012, Jiangsu

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2016年 / 44卷 / 05期

关键词：

Dictionary-learning; Gaussian mixture model; Ksvd; Sparse representation; Super-resolution;

D O I：

10.3969/j.issn.0372-2112.2016.05.025

中图分类号：

学科分类号：

摘要：

Image super-resolution reconstruction via Improved Dictionary Learning based on Coupled Feature Space is studied in the paper, in order to solve the following problems: 1 the dictionary training process is time-consuming, 2 the results are not satisfactory in the existing algorithms. In the proposed algorithm, at first, the Gaussian mixture model clustering algorithm is employed to cluster the training image blocks, secondly, quickly obtain high and low resolution feature space of dictionary and mapping matrix by using dictionary updating based on improved KSVD dictionary learning algorithm, and then, the Super-Resolution image is reconstructed according to the likelihood probability of test samples, in which each category adaptively selected the most matching dictionary and mapping matrix for high-resolution reconstruction, finally, the non-local similarity and iterative back-projection are exploited to furtherly improve the quality of the reconstruction image. The experimental results demonstrate the validity of the proposed algorithm. © 2016, Chinese Institute of Electronics. All right reserved.

引用

页码：1189 / 1195

页数：6

共 17 条

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