基于变分方法的超分辨率

A Super-Resolution Algorithm Based on Total Variation

基于全变分先验和变分分布.提出一个新颖的超分辨率算法,使用分级的贝叶斯框架,能够同时计算出重建的高分辨率图像和模型参数.本算法利用变分推论给出变量的后验分布近似.因为能够同时估计出模型参数,是自动的过程,无需对参数人工调节.实验结果表明所提算法在重建质量上优于当前主流的算法.

a novel algorithm for super resolution based on total variation prior and variational distribution approximations is proposed in this paper.We formulate the problem using a hierarchical Bayesian model where the reconstructed high resolution image and the model parameters are estimated simultaneously from the low resolution observations.The algorithm resulting from this formulation utilized variational inference and provides approximations to the posterior distributions of the latent variables.Due to the simultaneous parameter estimation,the algorithm is fully automated so parameter tuning is not required.Experimental results show that the proposed algorithm outperforms some of the state-of-the-art super resolution algorithms.