Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
University of Chinese Academy of Sciences · Institute of Automation
Abstract
Most modern face super-resolution methods resort to convolutional neural networks (CNN) to infer highresolution (HR) face images. When dealing with very low resolution (LR) images, the performance of these CNN based methods greatly degrades. Meanwhile, these methods tend to produce over-smoothed outputs and miss some textural details. To address these challenges, this paper presents a wavelet-based CNN approach that can ultra-resolve a very low resolution face image of 16 × 16 or smaller pixelsize to its larger version of multiple scaling factors (2×, 4×, 8× and even 16×) in a unified framework. Different from conventional CNN methods directly inferring HR images, our approach firstly learns to predict the…
Citation impact
- FWCI
- 13.12
- Percentile
- 100%
- References
- 47
Authors
4- HHHuaibo HuangCorresponding
University of Chinese Academy of Sciences, Institute of Automation
- RHRan He
Institute of Automation, University of Chinese Academy of Sciences
- ZSZhenan Sun
University of Chinese Academy of Sciences, Institute of Automation
- TTTieniu Tan
University of Chinese Academy of Sciences, Institute of Automation
Topics & keywords
- Convolutional neural network
- Wavelet
- Computer science
- Artificial intelligence
- Face (sociological concept)
- Pattern recognition (psychology)
- Wavelet transform
- Image resolution