Network Architecture Search for Face Enhancement
arXiv:2105.06528
Abstract
Various factors such as ambient lighting conditions, noise, motion blur, etc. affect the quality of captured face images. Poor quality face images often reduce the performance of face analysis and recognition systems. Hence, it is important to enhance the quality of face images collected in such conditions. We present a multi-task face restoration network, called Network Architecture Search for Face Enhancement (NASFE), which can enhance poor quality face images containing a single degradation (i.e. noise or blur) or multiple degradations (noise+blur+low-light). During training, NASFE uses clean face images of a person present in the degraded image to extract the identity information in terms of features for restoring the image. Furthermore, the network is guided by an identity-loss so that the identity in-formation is maintained in the restored image. Additionally, we propose a network architecture search-based fusion network in NASFE which fuses the task-specific features that are extracted using the task-specific encoders. We introduce FFT-op and deveiling operators in the fusion network to efficiently fuse the task-specific features. Comprehensive experiments on synthetic and real images demonstrate that the proposed method outperforms many recent state-of-the-art face restoration and enhancement methods in terms of quantitative and visual performance.
References in corpus (13)
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Neural Architecture Search with Reinforcement Learning
- Res2Net: A New Multi-scale Backbone Architecture
- Self-Attention Generative Adversarial Networks
- Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
- Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
- Deep Bilateral Learning for Real-Time Image Enhancement
- Efficient Neural Architecture Search via Parameter Sharing
- Hierarchical Representations for Efficient Architecture Search
- HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
- Multi-style Generative Network for Real-time Transfer
- Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks
- Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs