华中科技大学学报（自然科学版）2017，Vol.45Issue(6)：38-42,5.DOI:10.13245/j.hust.170608
一种基于L1/2正则约束的超分辨率重建算法
Super-resolution reconstruction based on L1/2 regularization
摘要
Abstract
In order to improve the quality of the reconstructed image and reduce the processing time, a single frame image super-resolution reconstruction algorithm based on L1/2 regularization constraint was proposed.At the stage of training sparse reconstruction dictionary, the wavelet coefficients single branch reconstruction method was effectively used to extract the features of low resolution image patches.At the stage of image reconstruction, the L1/2 regularization model was adopted to instead of the L1 norm, to solve the problem that the solution obtained by the L1 regularization model was often not sparse enough, and the quality of reconstructed image needed to be improved.A fast algorithm of L1/2 regularization for sparse solution was presented.The experimental results show that compared with the existing algorithms, the algorithm is better in the reconstruction of the subjective and objective evaluation of the image indicators, running speed and so on.关键词
重建图像/超分辨率/稀疏表示/L1/2正则模型/小波系数单支重构Key words
reconstructed image/super-resolution/sparse representation/L1/2 regularization model/single branch reconstruction of wavelet coefficients分类
信息技术与安全科学引用本文复制引用
徐志刚,李文文,朱红蕾,朱旭锋..一种基于L1/2正则约束的超分辨率重建算法[J].华中科技大学学报（自然科学版）,2017,45(6):38-42,5.基金项目
国家自然科学基金资助项目 (61363078) （61363078）
兰州理工大学 （）
