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.
CITATION STYLE
Wang, Y., Lu, T., Xu, R., & Zhang, Y. (2020). Face Super-Resolution by Learning Multi-view Texture Compensation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11962 LNCS, pp. 350–360). Springer. https://doi.org/10.1007/978-3-030-37734-2_29
Mendeley helps you to discover research relevant for your work.