群稀疏高斯洛伦兹混合先验超分辨率重建

Gauss-Lorenz hybrid prior super resolution reconstruction with mixed sparse representation

为了得到置信度更高的超分辨率先验模型,实现重建结果在噪声和细节之间的平衡,建立了基于混合稀疏表达框架下的高斯⎯洛伦兹混合先验模型。研究了该先验模型在超分算法中的应用优势和具体的应用方案。首先,根据先验信息的类型介绍了一些超分辨率算法的优势和问题。接着,提出对图像不同分量的统计特点进行单独建模的应用方法。然后,在分析了混合稀疏框架、高斯吉布斯先验和洛伦兹先验的基础上,说明了基于群稀疏框架下的高斯⎯洛伦兹混合先验的超分辨率算法。最后,介绍了具体实现环节和最终迭代方案。实验结果表明,本文基本完成了在重建过程中保持细节的同时抑制噪声的改进目标,可以用于更多复杂环境的超分辨率重建要求。

In order to obtain a super-resolution prior model with higher confidence and balance the reconstructed results between noise and details,this paper establishes a Gauss-Lorenz hybrid prior model based on the mixed sparse representation framework.This prior model's advantages and specific application schemes are studied.Firstly,according to the type of prior information,the advantages and problems of some traditional algorithms are introduced.Next,the statistical characteristics of different components of the image are modeled separately.Then,based on the analysis of the mixed sparse framework,the Gauss-Gibbs prior and the Lorenz prior,the super-resolution algorithm based on the Gauss-Lorenz hybrid prior under the group sparse framework is illustrated.Finally,the implementation and the final iteration scheme are introduced.The aim of noise suppression while maintaining details in the reconstruction process has been completed,which can be used for in more complex en-vironments with super-resolution resconstruction.