A novel reconstruction model for multi-frame super-resolution image based on l(mix) prior

作者：Chen Chuanbo; Liang Hu; Zhao Shengrong^*; Lyu Zehua; Sarem Mudar

来源：Computers & Electrical Engineering, 2014, 40(8): 142-153.

DOI：10.1016/j.compeleceng.2014.07.011

• Multi-frame super-resolution
• Hierarchical Bayesian method
• H-1 model
• Total variation model
• l(mix) model

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 l(mix) model, which combines the total variation model and the H-1 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 l(mix) 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.

• 出版日期2014-11
• 单位华中科技大学