A Fast Kernel Regression Framework for Video Super-Resolution
Wensen Yu, Minghui Wang, Hua-Wen Chang, Shuqing Chen · KSII Transactions on Internet and Information Systems · 2014
A series of kernel regression (KR) algorithms, such as the classic kernel regression (CKR), the 2-and 3-D steering kernel regression (SKR), have been proposed for image and video super-resolution.In existing KR frameworks, a single algorithm is usually adopted and applied for a whole image/video, regardless of region characteristics.However, their performances and computational efficiencies can differ in regions of different characteristics.To take full advantage of the KR algorithms and avoid their disadvantage, this paper proposes a kernel regression framework for video super-resolution.In this framework, each video frame is first analyzed and divided into three types of regions: flat, non-flat-stationary, and non-flat-moving regions.Then different KR algorithm is selected according to the region type.The CKR and 2-D SKR algorithms are applied to flat and non-flat-stationary regions, respectively.For non-flat-moving regions, this paper proposes a similarity-assisted steering kernel regression (SASKR) algorithm, which can give better performance and higher computational efficiency than the 3-D SKR algorithm.Experimental results demonstrate that the computational efficiency of the proposed framework is greatly improved without apparent degradation in performance.