计算机技术与发展2016，Vol.26Issue(10)：17-21,5.DOI:10.3969/j.issn.1673-629X.2016.10.004
基于局部回归和自相似性的图像超分辨率重建
Image Super-resolution Reconstruction Based on Local Regression and Self-similarity
摘要
Abstract
In recent years,image super-resolution reconstruction based on samples has gradually become a hot research topic,which usual-ly uses the external training samples. The similarity between the test image and the training samples affects the reconstruction results to a certain extent. To solve this problem,a super-resolution image reconstruction algorithm based on local regression and self-similarity is proposed. This algorithm,which makes use of the self-similarity between images at different scales and reconstructs the image by establis-hing the first-order autoregressive model of the patches,could make full use of the information of the image itself,and replace the travers-al search of self-similar patches with the sparse representation method. So it can guarantee the reconstruction quality even the number of the self-similar patches is not enough. The experimental results show that the reconstruction quality of this algorithm is high. It can allevi-ate the false high-frequency problem brought by the external training samples to a certain extent and have a good tradeoff between the re-construction quality and reconstruction time.关键词
超分辨率/自相似性/局部回归/字典学习/稀疏表示Key words
super-resolution/self-similarity/local regression/dictionary learning/sparse representation分类
信息技术与安全科学引用本文复制引用
李欣,崔子冠,陈杰,朱秀昌..基于局部回归和自相似性的图像超分辨率重建[J].计算机技术与发展,2016,26(10):17-21,5.基金项目
国家自然科学基金青年基金项目(61501260) （61501260）
江苏省自然科学基金项目(BK20130867,BK20140891) （BK20130867,BK20140891）
江苏省高校自然科学基金项目(13KJB510020) （13KJB510020）
江苏省普通高校研究生科研创新计划(CXLX120474) （CXLX120474）
