Image Superresolution Based on Locally Adaptive Mixed-Norm

Abstract EN

In a typical superresolution algorithm, fusion error modeling, including registration error and additive noise, has a great influence on the performance of the super-resolution algorithms.

In this letter, we show that the quality of the reconstructed high-resolution image can be increased by exploiting proper model for the fusion error.

To properly model the fusion error, we propose to minimize a cost function that consists of locally and adaptively weighted L1- and L2-norms considering the error model.

Binary weights are used so as to adaptively select L1- or L2-norm, based on the local errors.

Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either L1- or L2-norm.