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全局和局部结构内容自适应正则化的单幅图像超分辨模型

DOI: 10.11834/jig.20150102

Keywords: 超分辨,正则化,稀疏性,结构方向自适应回归

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Abstract:

目的基于正则化的重建是单幅图像超分辨的重要方法之一。其中,如何构造合适的图像先验,增强超分辨重建过程中的边缘和纹理保持能力是该类方法的关键。提出一个全局和局部结构内容自适应正则化的单幅图像超分辨模型。方法该模型综合了图像梯度的全局非高斯性和局部结构方向自适应回归特性。首先,利用广义高斯分布拟合图像梯度模的重尾特性,由最大后验概率框架构造了图像全局内容感知的lα(0<α<1)范数稀疏性度量;然后,利用图像局部内容的各向异性相关性,给出基于Geman-McClure(GM)权函数加权的局部结构方向自适应回归先验;最后利用半二次惩罚和变量分裂法,设计了该优化模型快速求解的超分辨算法。结果实验结果表明:在客观评价上,本文方法在峰值信噪比与结构相似度两方面优于现有的一些超分辨方法,在主观视觉效果上,能够很好的恢复图像的纹理细节和边缘信息。结论基于全局和局部结构内容自适应正则化的单幅图像超分辨方法在保持图像边缘和恢复图像纹理细节方面取得较好的重建性能。

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