A novel reconstruction model for multi-frame super-resolution image based on lmixlmix prior

•The TV model is combined with H1 model to form the lmix model.•The lmix model can not only remove the noise, but also preserve the edges.•An lmix based prior model for super resolution reconstruction is proposed.•The convergence and the efficiency of the proposed method are also analyzed.

Multi-frame Super-Reconstruction (SR) is a technique for reconstructing a High-Resolution (HR) image by fusing a set of low-resolution images of the same scene. One of the most difficult problems in SR is to preserve the edges while removing the noise. Therefore, in this paper we propose a novel lmixlmix model, which combines the total variation model and the H1H1 model by using a pair of different weighting parameters. Also, a hierarchical Bayesian framework is used and the weighting parameters can be modeled together with the HR image and other parameters. Thus the weighting parameters are updated according to the global features of the HR image in iterations. In this way, the proposed lmixlmix model can not only preserve the edges but also it can remove the noise. Our experimental results show that the proposed method has much improvement over the existing methods.

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