Frequency Aware Face Hallucination Generative Adversarial Network with Semantic Structural Constraint
This work addresses the problem of generating high-quality face images from low-resolution inputs for applications like surveillance or forensics, but it is incremental as it builds on existing GAN and 3DMM approaches.
The paper tackles face hallucination by proposing a GAN-based progressive network that incorporates high-frequency components and 3D structural constraints to improve local features and depth in generated high-resolution images, outperforming state-of-the-art methods.
In this paper, we address the issue of face hallucination. Most current face hallucination methods rely on two-dimensional facial priors to generate high resolution face images from low resolution face images. These methods are only capable of assimilating global information into the generated image. Still there exist some inherent problems in these methods; such as, local features, subtle structural details and missing depth information in final output image. Present work proposes a Generative Adversarial Network (GAN) based novel progressive Face Hallucination (FH) network to address these issues present among current methods. The generator of the proposed model comprises of FH network and two sub-networks, assisting FH network to generate high resolution images. The first sub-network leverages on explicitly adding high frequency components into the model. To explicitly encode the high frequency components, an auto encoder is proposed to generate high resolution coefficients of Discrete Cosine Transform (DCT). To add three dimensional parametric information into the network, second sub-network is proposed. This network uses a shape model of 3D Morphable Models (3DMM) to add structural constraint to the FH network. Extensive experimentation results in the paper shows that the proposed model outperforms the state-of-the-art methods.