AN adaptive L1–L2 hybrid error model to super-resolution
Huihui Song, Lei Zhang, Peikang Wang, Kaihua Zhang, Xin Li · 2010
A hybrid error model with L1and L2norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L1and L2norm terms. Therefore, the proposed hybrid model can have the advantages of both L1norm minimization (i.e. edge preservation) and L2norm minimization (i.e. smoothing noise). In addition, an effective convergence criterion is proposed, which is able to terminate the iterative L1and L2norm minimization process efficiently. Experimental results on images corrupted with various types of noises demonstrate the robustness of the proposed algorithm and its superiority to representative algorithms.