Liu Licheng, Chen C L Philip, Li Shutao
IEEE Trans Cybern. 2022 Jan;52(1):265-277. doi: 10.1109/TCYB.2020.2979320. Epub 2022 Jan 11.
Recently, learning-based representation techniques have been well exploited for grayscale face image hallucination. For color images, the previous methods only handle the luminance component or each color channel individually, without considering the abundant correlations among different channels as well as the inherent geometrical structure of data manifold. In this article, we propose a learning-based model in quaternion space with graph representation for color face hallucination. Instead of the spatial domain, the color image is represented in the quaternion domain to preserve correlations among different color channels. Moreover, a quaternion graph is learned to smooth the quaternion feature space, which helps to not only stabilize the linear system but also enclose the inherent topology structure of quaternion patch manifold. Besides, considering that single low-resolution (LR) image patch can just provide limited informative information in representation, we propose to simultaneously encode the query smaller LR patch as well as a larger patch containing the surrounding pixels seated at the same position in the objective. The larger patch with rich patterns is used to compensate the lost information in the query LR patch, which further enhances the manifold consistency assumption between the LR and HR patch spaces. The experimental results demonstrated the efficiency of the proposed method in hallucinating color face images.
最近，基于学习的表示技术已被充分用于灰度人脸图像超分辨率重建。对于彩色图像，先前的方法仅单独处理亮度分量或每个颜色通道，而没有考虑不同通道之间丰富的相关性以及数据流形的固有几何结构。在本文中，我们提出了一种在四元数空间中基于学习的模型，用于彩色人脸超分辨率重建，并采用图表示。彩色图像在四元数域中表示，而不是在空间域中表示，以保留不同颜色通道之间的相关性。此外，学习一个四元数图来平滑四元数特征空间，这不仅有助于稳定线性系统，而且有助于包围四元数补丁流形的固有拓扑结构。此外，考虑到单个低分辨率（LR）图像补丁在表示中只能提供有限的信息，我们建议同时对查询的较小LR补丁以及包含目标中相同位置周围像素的较大补丁进行编码。具有丰富模式的较大补丁用于补偿查询LR补丁中丢失的信息，这进一步增强了LR和HR补丁空间之间的流形一致性假设。实验结果证明了该方法在彩色人脸图像超分辨率重建中的有效性。
