自动化学报Issue(10)：2233-2244,12.DOI:10.3724/SP.J.1004.2014.02233
基于多尺度非局部约束的单幅图像超分辨率算法
Single-image Super-resolution Algorithm Based on Multi-scale Nonlocal Regularization
摘要
Abstract
Multi-scale structural self-similarity refers to that there are many similar structures in the same image, which are either in the same scale or across different scales. In this paper, a single-image super-resolution method based on multi-scale nonlocal regularization is proposed. In this method, the multi-scale nonlocal and the multi-scale dictionary learning methods are combined to add the extra information exploited from multi-scale similar structures into the reconstructed image. The multi-scale nonlocal method exploits extra information from multi-scale similar structures by searching for similar patches in the image pyramid and constructing the multi-scale nonlocal regularization according to the correspondence between multi-scale similar patches. The multi-scale dictionary learning method exploits extra information from multi-scale similar structures by using the image pyramid as training samples in dictionary learning, so that the patches in the pyramid have sparse representations over the learned dictionary. Experimental results demonstrate that the method achieves better image quality compared with ScSR, SISR, NLIBP, CSSS, ASDSAR and mSSIM methods.关键词
超分辨率/多尺度结构自相似性/稀疏表示/非局部方法Key words
Super-resolution (SR)/multi-scale structural self-similarity/sparse representation/nonlocal method引用本文复制引用
潘宗序,禹晶,肖创柏,孙卫东..基于多尺度非局部约束的单幅图像超分辨率算法[J].自动化学报,2014,(10):2233-2244,12.基金项目
国家自然科学基金(61171117),国家科技支撑计划项目(2012BAH31B01),中国博士后科学基金(2013M540946),北京市教育委员会科技计划重点项目(KZ201310028035)资助Supported by National Natural Science Foundation of China (61171117), National Science and Technology Pillar Program of China (2012BAH31B01), the Postdoctoral Science Foundation of China (2013M540946) and Key Project of the Science and Technology Development Program of Beijing Education Com-mittee of China (KZ201310028035) （61171117）
