Face Super-Resolution by Learning Multi-view Texture Compensation

Single face image super-resolution (SR) methods using deep neural network yields decent performance. Due to the posture of face images, multi-view face super-resolution task is more challenging than single input. Multi-view face images contain complement information from different view. However, it is hard to integrate texture information from multi-view low-resolution (LR) face images. In this paper, we propose a novel face SR using multi-view texture compensation to combine multiple face images to yield a HR image as output. We use texture attention mechanism to transfer high-accurate texture compensation information to fixed view for better visual performance. Experimental results conform that the proposed neural network outperforms other state-of-the-art face SR algorithms.