降级(电信)
解耦(概率)
面子(社会学概念)
图像分辨率
计算机科学
计算机视觉
人工智能
工程类
电信
社会科学
控制工程
社会学
作者
Xin Ding,Zheng Wang,Jing Fang,Zhenyu Shu,Ruimin Hu,Chia‐Wen Lin
标识
DOI:10.1109/tcsvt.2023.3325357
摘要
Face enhancement aims to improve low-quality face images to a higher-quality level. However, in real-world nighttime scenes, complex degradation factors often affect these images, making it challenging to preserve important facial details. Existing image enhancement algorithms typically focus on independently conducting image super-resolution and brightness enhancement, assuming a fixed degradation level based on simulated training datasets. Nonetheless, real nighttime scenes involve complex degradation processes, where degradation factors dynamically and variably manifest. Therefore, achieving effective face enhancement in such scenarios is particularly daunting. This work analyzes and unveils the multiple factors of low resolution and low illumination during degradation. Based on this analysis, we propose a Bi-factor Degradation Decoupling network. Our method leverages a decoupling network to generate qualitative and quantitative features corresponding to each factor's degradation degree in the low-quality environment. These features are then combined with robust facial feature constraints to recover the details of low-quality faces. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches in both enhancement and face super-resolution.
科研通智能强力驱动
Strongly Powered by AbleSci AI