image superresolution based on locally adaptive mixed-norm

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.