Face Hallucination Using Bilateral-Projection-Based Two-Dimensional Principal Component Analysis

摘要

In this paper, we propose a new super-resolution face hallucination method based on Bilateral-projection-based Two-Dimensional Principal Component Analysis (B2DPCA). Firstly, the high-resolution (HR) face image and its corresponding low-resolution (LR) face image are projected to the HR and LR B2DPCA feature spaces, respectively. In these spaces, the linear mixing relationship between HR and LR feature is estimated from a training set. For reconstructing the HR image from the observed LR image, the LR image is firstly projected to LR feature space and then mapped to HR feature. Finally, the HR feature is reconstructed to the HR face image. Experiments on the well-known face databases show that the performance of our proposed method. The resolution and quality of the hallucinated face images are greatly enhanced over the LR ones, which is very helpful for human recognition.