基于多字典学习和图像块映射的超分辨率重建

doi:10.11772/j.issn.1001-9081.2016.05.1394

计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1394-1398.DOI: 10.11772/j.issn.1001-9081.2016.05.1394

基于多字典学习和图像块映射的超分辨率重建

莫建文¹, 曾儿孟¹, 张彤², 袁华¹

1. 桂林电子科技大学信息与通信学院, 广西桂林 541004;
2. 桂林电子科技大学机电工程学院, 广西桂林 541004

收稿日期:2015-11-04 修回日期:2015-12-15 出版日期:2016-05-10 发布日期:2016-05-09
通讯作者: 曾儿孟
作者简介:莫建文(1972-),男,广西平男人,副教授,博士,主要研究方向:图像处理、模式识别、智能信息处理;曾儿孟(1988-),男,广西贵港人,硕士研究生,主要研究方向:数字图像处理;张彤(1973-),男,广西桂林人,副教授,硕士,主要研究方向:智能信息处理、图像处理;袁华(1975-),男,湖北宜昌人,讲师,硕士,主要研究方向:图像处理、智能信息处理。
基金资助:
国家自然科学基金资助项目(61362021);广西自然科学基金资助项目(2013GXNSFDA019030,2013GXNSFAA019331,2014GXNSFDA118035);广西科技开发项目(桂科攻1348020-6,桂科能1298025-7);广西教育厅项目(201202ZD044,2013YB091);桂林市科技攻关项目(20130105-6,20140103-5);桂林电子科技大学研究生教育创新计划项目(YJCXS201534)。

Super-resolution reconstruction based on multi-dictionary learning and image patches mapping

MO Jianwen¹, ZENG Ermeng¹, ZHANG Tong², YUAN Hua¹

1. School of Information and Communication, Guilin University of Electronic Technology, Guilin Guangxi 541004, China;
2. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin Guangxi 541004, China

Received:2015-11-04 Revised:2015-12-15 Online:2016-05-10 Published:2016-05-09
Supported by:
This work is partially supported by the Natural Science Foundation of China (61362021), the Natural Science Foundation of Guangxi(2013GXNSFDA019030,2013GXNSFAA019331,2014GXNSFDA118035), the Scientific and Technological Development Project of Guangxi (1348020-6,1298025-7), Project of Guangxi Education Department(201202ZD044,2013YB091), the Scientific and Technological Project of Guilin(20130105-6,20140103-5), Innovation Project of GUET Graduate Education(YJCXS201534).

摘要/Abstract

摘要： 针对单一冗余字典在稀疏表示图像超分辨率重建结果出现不清晰、伪影以及重建过程编码效率不高、运算时间过长的问题,提出一种基于多字典学习和图像块映射的超分辨率重建方法。该方法在传统稀疏表示的框架下,首先探索局部图像块的梯度结构信息,按梯度角度将训练样本块分类;然后为每个子类样本集学习高低分辨率字典对,再结合最近邻思想应用生成的字典,为每个子类计算从低分辨率块到高分辨率块映射的函数;最后将重建过程简化为输入块和映射函数的乘积,在保证提高重建质量的同时减少了图像重建的时间。实验结果表明,所提算法在视觉效果有较大的提升,同时与锚点邻域回归算法相比,评价参数峰值信噪比(PSNR)平均提高约0.4 dB。

关键词: 稀疏表示, 图像块分类, 多字典学习, 映射函数, 邻域嵌入

Abstract: To overcome the disadvantages of the unclear results and time consuming in the sparse representation of image super-resolution reconstruction with single redundant dictionary, a single image super-resolution reconstruction method based on multi-dictionary learning and image patches mapping was proposed. In the framework of the traditional sparse representation, firstly the gradient structure information of local image patches was explored, and a large number of training image patches were clustered into several groups by their gradient angles, from those clustered patches the corresponding dictionary pairs were learned. And then the mapping function was computed from low resolution patch to high resolution patch in each clustered group via learned dictionary pairs with the idea of neighbor embedding. Finally the reconstruction process was reduced to a projection of each input patch into the high resolution space by multiplying with the corresponding precomputed mapping function, which improved the images quality with less running time. The experimental results show that the proposed method improves the visual quality significantly, and increases the PSNR (Peak Signal-to-Noise Ratio) at least 0.4 dB compared with the anchored neighborhood regression algorithm.

Key words: sparse representation, image patches classification, multi-dictionary learning, mapping function, neighbor embedding

中图分类号:

TP391

莫建文, 曾儿孟, 张彤, 袁华. 基于多字典学习和图像块映射的超分辨率重建[J]. 计算机应用, 2016, 36(5): 1394-1398.

MO Jianwen, ZENG Ermeng, ZHANG Tong, YUAN Hua. Super-resolution reconstruction based on multi-dictionary learning and image patches mapping[J]. Journal of Computer Applications, 2016, 36(5): 1394-1398.

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基于多字典学习和图像块映射的超分辨率重建

Super-resolution reconstruction based on multi-dictionary learning and image patches mapping

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