Zhang Jiyan, Sun Hua, Fan Haiyang, Xiong Yujie, Zhang Jiaqi
School of Software, Xinjiang University, Urumqi 830091, China.
J Imaging. 2025 Apr 29;11(5):138. doi: 10.3390/jimaging11050138.
Image super-resolution (SR) reconstruction is a critical task aimed at enhancing low-quality images to obtain high-quality counterparts. Existing denoising diffusion models have demonstrated commendable performance in handling image SR reconstruction tasks; however, they often require thousands-or even more-diffusion sampling steps, significantly prolonging the training duration for the denoising diffusion model. Conversely, reducing the number of diffusion steps may lead to the loss of intricate texture features in the generated images, resulting in overly smooth outputs despite improving the training efficiency. To address these challenges, we introduce a novel diffusion model named RapidDiff. RapidDiff uses a state-of-the-art conditional noise predictor (CNP) to predict the noise distribution at a level that closely resembles the real noise properties, thereby reducing the problem of high-variance noise produced by U-Net decoders during the noise prediction stage. Additionally, RapidDiff enhances the efficiency of image SR reconstruction by focusing on the residuals between high-resolution (HR) and low-resolution (LR) images. Experimental analyses confirm that our proposed RapidDiff model achieves performance that is either superior or comparable to that of the most advanced models that are currently available, as demonstrated on both the ImageNet dataset and the Alsat-2b dataset.
图像超分辨率（SR）重建是一项关键任务，旨在增强低质量图像以获得高质量的对应图像。现有的去噪扩散模型在处理图像SR重建任务方面已展现出值得称赞的性能；然而，它们通常需要数千步甚至更多的扩散采样步骤，这显著延长了去噪扩散模型的训练时长。相反，减少扩散步骤可能会导致生成图像中复杂纹理特征的丢失，尽管提高了训练效率，但输出结果会过度平滑。为应对这些挑战，我们引入了一种名为RapidDiff的新型扩散模型。RapidDiff使用先进的条件噪声预测器（CNP）来预测与真实噪声属性极为相似水平的噪声分布，从而减少了U-Net解码器在噪声预测阶段产生的高方差噪声问题。此外，RapidDiff通过关注高分辨率（HR）和低分辨率（LR）图像之间的残差来提高图像SR重建的效率。实验分析证实，我们提出的RapidDiff模型在ImageNet数据集和Alsat-2b数据集上的表现均优于或与当前最先进的模型相当。
