Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

• URL: http://arxiv.org/abs/2211.10532v1
• Date: Fri, 18 Nov 2022 23:16:57 GMT
• Title: Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network
• Authors: Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu, Yu Liu, Shan Du
• Abstract summary: We propose a novel face super-resolution method, namely Semantic guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Block (RIDB) Experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.
• Score: 15.102899995465041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4x and 8x). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons.

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