Review of real-world face super-resolution algorithms
Face super-resolution enhances resolution and quality of low-resolution facial images,finding wide applications in fields of video surveillance,criminal investigation,and entertainment.However,in real-world scenarios,imperfect imaging systems,recording equipment,transmission media,and processing methods resulted in irregular combinations of degradation processes that reduce image quality,like noise and blurring.Training network models based on explicit degradation processes alone cannot meet practical needs.This paper reviews the principles of non-blind and blind face super-resolution techniques,the commonly used datasets and evaluation metrics and the subjective and objective reconstruction results of representative works in the field of face super-resolution.Future related researches should focus on multi-modal information decision fusion and tensor fusion to improve the feature dimension and temporal similarity of the reconstructed images;enhance the generalization ability of the model through large-scale pre-training and adversarial learning;investigate the impact of identity consistency algorithms and technologies such as transfer learning on complex imaging conditions.
face super-resolutionreal-worlddeep learningdegradation process
国家科技期刊平台
NSTL
万方数据
