Face Hallucination via Convolution Neural Network
Nie Hui, Yao Lu, Javaria Ikram · 2016
Deep learning methods have been successfully used in many areas of computer vision, including super resolution. However, all of the previous deep learning methods have been proposed for generic image super resolution. In this paper, we proposed to use convolutional neural network for face hallucination (FH) by combining the domain specific prior knowledge of face images and properties of deep learning. In the proposed method, an end to end mapping is learned as a deep convolutional network between the low resolution (LR) images and their corresponding high resolution (HR) images to upscale the input face image directly. In order to achieve larger magnification factor, we consider to cascade several convolution neural networks each of which is with a fixed up-scaling factor and upscales the LR image step by step. Experimental result shows that our proposed method can achieve better performance comparing to the traditional face hallucination methods.