A multi-frame adaptive super-resolution method using double channel and regional pixel information

Multi-frame image super-resolution (SR) aims to utilize information from a set of low resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. In this paper, we propose a novel multi-frame image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new fidelity term is formed by combining L1 norm and L2 norm according to defined Residual Weight Parameters (RWP) and Channel Weight Parameters (CWP). A new regularization term which adopts Regional Adaptive Weight Coefficients (RAWC) is proposed to keep edge and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thorough experimental results show the new algorithm using both fidelity and regularization terms for SR reconstruction is effective than other methods.

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