计算机工程与应用2019，Vol.55Issue(20)：159-163,169,6.DOI:10.3778/j.issn.1002-8331.1806-0457
优化的AdaBoost回归图像超分辨方法
Optimized Regression-Based Image Super-Resolution Method via AdaBoost
摘要
Abstract
Example regression-based technique has been recognized as a simple but effective image Super-Resolution (SR)method. However, simple linear regression model often cannot represent the complex relationship between Low-Resolution(LR)and High-Resolution(HR)images well. At the same time, it can pose a memory issue that when the dic-tionary and regression size can grow to more than a gigabyte, limiting applicability in memory constrained scenarios. To address those problems, it proposes an optimized example-based SR method with weighted-feature via AdaBoost. In the training stage, it begins with the learning of a sparse dictionary as anchored points from a training set using K -SVD dic-tionary learning algorithm. And then the anchored neighborhood regression model is employed to build a set of strong regressors by using the AdaBoost regression algorithm with T rounds. Finally, the learned regressors are coded as a lin-ear combination of few basis regressors for SR reconstruction. To verify the effectiveness of the proposed SR method, four publicly available datasets are used to compare the SR performance with other state-of-art methods. The experimental results show that the proposed algorithm gains better reconstruction performance and lower memory usage, as well as it’s better to the compared algorithms in terms of both objective and visual quality assessments.关键词
AdaBoost/锚点邻域回归/字典学习/K-SVDKey words
AdaBoost/anchored neighbor regression/dictionary learning/K-SVD分类
信息技术与安全科学引用本文复制引用
张凯兵,王珍,闫亚娣,朱丹妮..优化的AdaBoost回归图像超分辨方法[J].计算机工程与应用,2019,55(20):159-163,169,6.基金项目
国家自然科学基金(No.61471161) （No.61471161）
陕西省科技厅自然科学基础研究重点项目(No.2018JQ1017) （No.2018JQ1017）
西安工程大学博士科研启动基金(No.BS1616). （No.BS1616）
