Enhancing Face Image Quality: Strategic Patch Selection With Deep Reinforcement Learning and Super-Resolution Boost via RRDB

Abstract

Facial super-resolution (FSR) is a critical research area whose goal is to improve visual quality by converting low-resolution facial images to high resolution ones. Research in FSR has come a long way thanks to advances in deep learning technologies. However, there is still a need to develop effective methods for revealing facial details and preserving the overall appearance. For this purpose, a new approach called Deep Reinforcement Learning Based Super Resolution of Face Regions (DRL-SRFR) is proposed. It is based on Deep Reinforcement Learning (DRL) and Deep Residual Dense Block (RRDB) architectures. In the DRL part of the method, new regions that need attention are identified at each step using the repeated visual attention methodology. The details in different parts of the face image are iteratively improved to produce more natural and high-quality face images. In addition, with the stochastic action-taking process, the decision-making process is made flexible by focusing on important facial regions. The focused region is improved with the RRDB structure using dense connections and residual learning. Experiments and ablation studies show that the developed model provides a significant advantage over existing methods in improving local details and preserving appearance integrity.