QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution
Journal:
arXiv
Published Date:
Mar 15, 2025
Abstract
Deep learning-based super-resolution (SR) methods often perform pixel-wise
computations uniformly across entire images, even in homogeneous regions where
high-resolution refinement is redundant. We propose the Quadtree Diffusion
Model (QDM), a region-adaptive diffusion framework that leverages a quadtree
structure to selectively enhance detail-rich regions while reducing
computations in homogeneous areas. By guiding the diffusion with a quadtree
derived from the low-quality input, QDM identifies key regions-represented by
leaf nodes-where fine detail is essential and applies minimal refinement
elsewhere. This mask-guided, two-stream architecture adaptively balances
quality and efficiency, producing high-fidelity outputs with low computational
redundancy. Experiments demonstrate QDM's effectiveness in high-resolution SR
tasks across diverse image types, particularly in medical imaging (e.g., CT
scans), where large homogeneous regions are prevalent. Furthermore, QDM
outperforms or is comparable to state-of-the-art SR methods on standard
benchmarks while significantly reducing computational costs, highlighting its
efficiency and suitability for resource-limited environments. Our code is
available at https://github.com/linYDTHU/QDM.
