Abstract
Single image super-resolution (SISR) aims to reconstruct high-quality images from low-resolution inputs, a persistent challenge in computer vision with critical applications in medical imaging, satellite imagery, and video enhancement. Traditional diffusion model-based (DM-based) methods, while effective in restoring fine details, suffer from computational inefficiency due to their iterative denoising process. To address this, we introduce the Sparse Momentum-based Faster Diffusion Model (SMFDM), designed for rapid and high-fidelity super-resolution. SMFDM integrates a novel encoder state reuse mechanism that selectively omits non-critical time steps during the denoising phase, significantly reducing computational redundancy. Additionally, the model employs a sparse momentum mechanism, enabling robust representation capabilities while utilizing only a fraction of the original model weights. Experiments demonstrate that SMFDM achieves an impressive 71.04% acceleration in the diffusion process, requiring only 15% of the original weights, while maintaining high-quality outputs with effective preservation of image details and textures. Our work highlights the potential of combining sparse learning and efficient sampling strategies to enhance the practical applicability of diffusion models for super-resolution tasks.