Liu Licheng, Chen C L Philip, Wang Yaonan
IEEE Trans Neural Netw Learn Syst. 2023 May;34(5):2490-2502. doi: 10.1109/TNNLS.2021.3106773. Epub 2023 May 2.
Manifold learning-based face hallucination technologies have been widely developed during the past decades. However, the conventional learning methods always become ineffective in noise environment due to the least-square regression, which usually generates distorted representations for noisy inputs they employed for error modeling. To solve this problem, in this article, we propose a modal regression-based graph representation (MRGR) model for noisy face hallucination. In MRGR, the modal regression-based function is incorporated into graph learning framework to improve the resolution of noisy face images. Specifically, the modal regression-induced metric is used instead of the least-square metric to regularize the encoding errors, which admits the MRGR to robust against noise with uncertain distribution. Moreover, a graph representation is learned from feature space to exploit the inherent typological structure of patch manifold for data representation, resulting in more accurate reconstruction coefficients. Besides, for noisy color face hallucination, the MRGR is extended into quaternion (MRGR-Q) space, where the abundant correlations among different color channels can be well preserved. Experimental results on both the grayscale and color face images demonstrate the superiority of MRGR and MRGR-Q compared with several state-of-the-art methods.
在过去几十年中，基于流形学习的人脸超分辨率技术得到了广泛发展。然而，由于最小二乘回归，传统学习方法在噪声环境中往往失效，这种回归通常会为用于误差建模的噪声输入生成失真的表示。为了解决这个问题，在本文中，我们提出了一种用于噪声人脸超分辨率的基于模态回归的图表示（MRGR）模型。在MRGR中，基于模态回归的函数被纳入图学习框架，以提高噪声人脸图像的分辨率。具体来说，使用基于模态回归的度量代替最小二乘度量来规范编码误差，这使得MRGR能够对具有不确定分布的噪声具有鲁棒性。此外，从特征空间学习图表示，以利用补丁流形的固有类型结构进行数据表示，从而得到更准确的重建系数。此外，对于噪声彩色人脸超分辨率，MRGR被扩展到四元数（MRGR-Q）空间，在该空间中可以很好地保留不同颜色通道之间的丰富相关性。在灰度和彩色人脸图像上的实验结果表明，与几种最新方法相比，MRGR和MRGR-Q具有优越性。
