Reinforced Diffusion for Image Denoising
Analysis
This paper introduces a novel approach to image denoising by combining anisotropic diffusion with reinforcement learning. It addresses the limitations of traditional diffusion methods by learning a sequence of diffusion actions using deep Q-learning. The core contribution lies in the adaptive nature of the learned diffusion process, allowing it to better handle complex image structures and outperform existing diffusion-based and even some CNN-based methods. The use of reinforcement learning to optimize the diffusion process is a key innovation.
Key Takeaways
“The diffusion actions selected by deep Q-learning at different iterations indeed composite a stochastic anisotropic diffusion process with strong adaptivity to different image structures, which enjoys improvement over the traditional ones.”