Select & Enhance: Masked-based image enhancement through tree-search theory and deep reinforcement learning.

The enhancement of low-quality images is both a challenging task and an essential endeavor in many fields including computer vision, computational photography, and image processing. In this paper, we propose a novel and fully explainable method for image enhancement that combines spatial selection and histogram equalization. Our approach leverages tree-search theory and deep reinforcement learning to iteratively select areas to be processed. Extensive experimentation on two datasets demonstrates the quality of our method compared to other state-of-the-art models. We also conducted a multi-user experiment which shows that our method can emulate a variety of enhancement styles. These results highlight the effectiveness and versatility of the proposed method in producing high-quality images through an explainable enhancement process. • A fully explainable image enhancement method based on reinforcement learning. • The method alternates spatial selection and histogram equalization through deep RL. • An extensive experimentation shows that our method is competitive with SOTA methods. [ABSTRACT FROM AUTHOR]