RoughSet-DDPM: An Image Super-Resolution Method Based on Rough set Denoising Diffusion Probability Model

Abstract

Image super-resolution aims to generate high-resolution (HR) images from low-resolution (LR) inputs. Existing methods like autoregressive models, generative adversarial networks (GANs), and denoising diffusion probability models (DDPMs) have limitations in image quality or sampling efficiency. This paper proposes Rough Set-DDPM, a new super-resolution technique combining rough set theory and DDPMs. The rough set formulation divides the DDPM sampling sequence into optimal sub-columns by minimizing roughness of sample sets. Particle swarm optimization identifies the sub-columns with lowest roughness. Rough Set-DDPM applies iterative denoising on these optimal columns to output HR images. Experiments on the FFHQ dataset validate that Rough Set-DDPM improves DDPM sampling efficiency while maintaining image fidelity. Quantitative results show Rough Set-DDPM requires fewer sampling steps and generates higher quality HR images compared to autoregressive models and GANs. By enhancing DDPM sampling, Rough Set-DDPM provides an effective approach to super-resolution that balances image quality and sampling speed. The key contributions include introducing rough sets to optimize DDPM sampling and demonstrating superior performance over existing methods.

Keywords

• image super-resolution
• probability model for denoising diffusion
• rough set
• U-net network