Multi-step reinforcement learning for medical image super-resolution
Alix Bouffard, Mihaela Pop, Mehran Ebrahimi · 2023
We apply a multi-agent Reinforcement Learning (RL) algorithm to single image super-resolution (SISR). In our novel implementation, each agent chooses a particular action from a fixed action set comprised of existing local enhancement operators to update each pixel intensity value. The pixel-wise arrangement of agents enables the algorithm to increase the resolution of an image by choosing optimal pixel intensity values from each option in a content-aware manner. While previous implementations of the model on SISR use Generative Adversarial Network (GAN)-based algorithms, we demonstrate that local operators can produce promising improved results without relying on the additional overhead of using machine learning techniques for the action space. Notably, we apply the proposed method to medical images, whereas previous implementations focused on natural images.