  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.
