欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
基于图像在过完备字典下的稀疏表示,建立了稀疏性正则化的多帧图像超分辨凸变分模型。模型中的正则项刻画了理想图像的稀疏性先验约束,保真项度量其在退化模型下与观测图像的一致性。基于线性化Bregman方法,将正则项替换为其Bregman距离,对保真项进行线性化,从而可将原问题解耦,进而提出求解该模型的两步迭代算法：第一步为仅对正则项的阈值收缩操作,第二步为仅对保真项的梯度下降操作。此方法大幅度降低了计算复杂性,并能够对噪声保持鲁棒。实验结果表明,只需较少次数的迭代就可获得很好的超分辨重建结果,验证了本文模型与算法的有效性。
英文摘要：
In terms of sparse representations of the underlying image in an over-complete dictionary,a sparsity regularized convex variational model for multi-frame image super-resolution is proposed.The regularization term constrains the underlying image to have a sparse representation in a proper over-complete dictionary.The fidelity term restricts the consistency with the measured image in terms of the data degradation model.Furthermore,by replacing the regularization term with its Bregman distance and linearizing the fidelity term,this convex variational problem is decoupled and a fast two step numerical iteration algorithm is proposed to solve it in terms of the linearized Bregman method.The first step is threshold shrinkage with respect to only the regularization term and the second step is to use the gradient descent dealing with only the fidelity term,thus the numerical complexity is decreased rapidly and is robust to noise.Numerical results for optics images demonstrate that only a few iterations can obtain very well results,thus both our super-resolution model and numerical algorithm are effective.
同期刊论文项目
同项目期刊论文
位置：