光学学报, 2018, 38 (9): 0910002, 网络出版: 2019-05-09   

协作稀疏字典学习实现单幅图像超分辨率重建 下载: 881次

Collaborative Sparse Dictionary Learning for Reconstruction of Single Image Super Resolution
邱康 1,*易本顺 1,2,*向勉 1肖进胜 1,2
作者单位
1 武汉大学电子信息学院, 湖北 武汉 430072
2 地球空间信息技术协同创新中心, 湖北 武汉 430079
摘要
字典的选择影响基于稀疏编码的图像超分辨率重建模型的重建质量。提出了一种基于协作稀疏表达的字典学习算法。在训练阶段,通过K-Means聚类算法将样本图像块划分为不同的聚类;构建基于同时稀疏约束条件的协作稀疏字典学习模型对每个聚类训练高、低分辨率字典;应用基于L2范数的稀疏编码模型将图像超分辨率重建过程中输入图像块由低分辨率到高分辨率的映射转变为简单的线性映射,并针对不同聚类求得相应的线性映射矩阵。在重建阶段,输入图像块通过搜索与自身结构最相似的聚类来选择相应映射矩阵获得重建后的高分辨率图像。结果表明,本文算法通过改进字典学习过程实现了更好的图像超分辨率重建质量。
Abstract
Performance of sparse coding based on image super resolution reconstruction model is influenced by dictionary selection. A dictionary learning algorithm based on collaborative sparse representation is proposed. In training stage, training image patches are grouped into different clusters by applying K-means clustering algorithm. A series of high- and low- resolution dictionaries are trained over every clusters by collaborative sparse dictionary learning model which is based on constraint of simultaneously sparse. The complex mapping relationship between low-and high-resolution image patches is transformed into a simple linear mapping by using an L2-norm based sparse coding model, and a series of mapping matrices corresponding to each different clusters are obtained. In reconstruction stage, each input image patch is mapped to a high-resolution patch by a mapping matrix which is selected by searching out the cluster with largest similarity to input patch. Experimental results show that the proposed method achieves better reconstruction quality by improving the dictionary learning process.

邱康, 易本顺, 向勉, 肖进胜. 协作稀疏字典学习实现单幅图像超分辨率重建[J]. 光学学报, 2018, 38(9): 0910002. Kang Qiu, Benshun Yi, Mian Xiang, Jinsheng Xiao. Collaborative Sparse Dictionary Learning for Reconstruction of Single Image Super Resolution[J]. Acta Optica Sinica, 2018, 38(9): 0910002.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!