A locally adaptive L1−L2 norm for multi-frame super-resolution of images with mixed noise and outliers

• A locally adaptive norm regularized method for super-resolution is proposed.
• The adaptive norm for the fidelity is chosen based on outliers’ detection.
• A weight to balance different norm constraints is estimated adaptively.
• The experiments show its superiority compared with other popular variational methods.

In this paper, we present a locally adaptive regularized super-resolution model for images with mixed noise and outliers. The proposed method adaptively assigns the local norms in the data fidelity term of the regularized model. Specifically, it determines different norm values for different pixel locations, according to the impulse noise and motion outlier detection results. The L1L1 norm is employed for pixels with impulse noise and motion outliers, and the L2L2 norm is used for the other pixels. In order to balance the difference in the constraint strength between the L1L1 norm and the L2L2 norm, a strategy to adaptively estimate a weighted parameter is put forward. The experimental results confirm the superiority of the proposed method for different images with mixed noise and outliers.