Image superresolution based on locally adaptive mixed-norm (Q983405)

Summary: 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 \(L_{1}\)- and \(L_{2}\)-norms considering the error model. Binary weights are used so as to adaptively select \(L_{1}\)- or \(L_{2}\)-norm, based on the local errors. Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either \(L_{1}\)- or \(L_{2}\)-norm.