DMKPN: Image Deblurring Under Multi-Factor Aliasing Diffusion Degradation

Abstract

Image degradation results from a combination of factors. Recently, CNN-based image deblurring methods have made significant progress, but they rely heavily on the accuracy of paired data, which is impractical to collect for every camera. To address this, we propose a physical model for natural images that applies to various cameras. This model considers the diffusion effects of multiple factors during degradation and effectively simulates the degraded state of natural images. We then design the Defocus Map-based Kernel Prediction Network (DMKPN) for adaptive image quality enhancement. Specifically, we develop a DM-Attention Block to assist kernel prediction under the guidance of the defocus map and design Multi-Scale Modulation to filter information at each scale, making the most of image context. Additionally, Multi-Scale Loss is introduced to enhance network robustness. Experiments demonstrate that our method exhibits strong spatial adaptability and generates high-quality images with sharp edges.