Iterative Multi-Frame Super-Resolution Image Reconstruction via Variance-Based Fidelity to the Data

Multi-frame Super-Resolution (SR) image reconstruction creates a single High-Resolution (HR) image from a sequence of Low-Resolution (LR) frames. Apart from resolution increment, blurring and noise removal is also achieved. In stochastic regularized methods, the SR problem is formulated by means of two terms, the data-fidelity term and the regularization term. In the present work, a novel estimator named Var − norm has been proposed for utilization in the data-fidelity term. This estimator presents a simple mathematical form based on the variance of the SR estimation error, i.e. on the difference between the estimated LR frame and the corresponding measured LR frame. The introduced Var − norm estimator is combined with the Bilateral Total Variation (BTV) regularization to formulate a novel SR method. The SR performance of the proposed method is directly compared with that of two SR techniques existing in the literature. Experimentation proves that the proposed method outperforms the existing methods.