Multi-step reinforcement learning for medical image super-resolution

Alix Bouffard; Mihaela Pop; Mehran Ebrahimi

doi:10.1117/12.2653655

3 April 2023 Multi-step reinforcement learning for medical image super-resolution

Alix Bouffard, Mihaela Pop, Mehran Ebrahimi

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124641Y (2023) https://doi.org/10.1117/12.2653655
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Alix Bouffard, Mihaela Pop, and Mehran Ebrahimi "Multi-step reinforcement learning for medical image super-resolution", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641Y (3 April 2023); https://doi.org/10.1117/12.2653655

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