关键词: AdaBoost, 锚点邻域回归, 字典学习, [K]-SVD

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 dictionary 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 dictionary 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 linear 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.

Key words: AdaBoost, anchored neighbor regression, dictionary learning, [K]-SVD