2022/08/03 by Dohoon Ryu, Jong Chul Ye, Ryu, Dohoon +1 · 1 voice · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #cs.CV #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2208.01864
openalex publication_date 2022/08/03 · arxiv published 2022/08/03 · arxiv updated 2022/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal diffusion model that can generate high resolution images starting from much coarser resolution images using a \em single score function trained with a positional embedding. This enables a neural network to be much lighter and also enables time-efficient image generation without compromising its performances. Furthermore, we show that the proposed approach can be also efficiently used for multi-scale super-resolution problem using a single score function.