计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 298-302.doi: 10.11896/j.issn.1002-137X.2019.08.049
王淑云, 干宗良, 刘峰
WANG Shu-yun, GAN Zong-liang, LIU Feng
摘要: 人脸超分辨率重建是指从一幅低分辨率人脸图像重建出相应的高分辨率图像的过程。大部分的人脸超分辨率重建算法都假设输入图像是对齐且不含噪声的。当输入的人脸图像为非对齐时,超分辨率重建的性能将降低。为此,提出一种基于学习的层级聚类回归算法,其主要针对非对齐的单帧人脸图像的超分辨率重建。该算法分为两部分:聚类和回归。聚类阶段,将训练样本的尺寸统一成某个小尺寸的人脸图像,用于训练人脸图像字典。该字典的字典原子为聚类中心,对原始的人脸图像进行聚类,得到各个子空间的人脸图像簇。该算法充分利用了人脸结构的先验信息,能获得更准确的聚类结果。在回归阶段,仅需要训练一个全局字典,各个子空间的人脸图像共享这些字典原子。在每个簇内,搜索各个驻点的邻域,以生成对应的邻域子空间。然后,学习低分辨率与高分辨率样本特征之间的映射关系,以得到每个子空间的回归模型。该算法的核心是所有的人脸图像类共享一个全局字典,但对于同一个驻点,在不同的人脸图像簇内,邻域样本各不相同,这样能够更准确地学习局部映射关系。该算法不仅可以缩短训练时间,还可以提高人脸超分辨率重建的质量。对比实验的结果表明,该算法的PSNR至少可以提升0.39dB,SSIM可以提升0.01~0.18。
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