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 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. © 2010 o. a. omer and t. tanaka.