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- 2016
非局域自相似约束的Shearlet稀疏正则化图像恢复
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Abstract:
提出一种结合非局部自相似和Shearlet稀疏性正则化的图像恢复变分模型。模型采用观测图像与待恢复图像的能量误差为保真项,联合Shearlet稀疏性和非局域自相似性为混合正则化项。正则化项同时兼顾图像的变换特性和自身结构全局特性。基于变量分裂增广拉格朗日法提出了求解该变分模型的数值算法。以图像去模糊和图像修复为例,对算法性能进行了测试。实验结果表明,该模型和所提算法能够较好地恢复图像,与其他算法相比,可获得更高的峰值信噪比(PSNR)和结构自相似指标(SSIM),具有更好的视觉效果。
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