An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Image Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
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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.