标题： 四元数小波条件扩散模型用于图像超分辨率 显示英文标题

标题： Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution

摘要： 图像超分辨率是计算机视觉中的一个基础问题，其应用范围从医学影像到卫星分析。 从低分辨率输入重建高分辨率图像的能力对于增强下游任务（如目标检测和分割）至关重要。 虽然深度学习显著推进了超分辨率（SR），但在高放大因子下实现具有精细细节和逼真纹理的高质量重建仍然具有挑战性。 最近利用扩散模型的方法展示了有希望的结果，但它们通常难以在感知质量与结构保真度之间取得平衡。 在这项工作中，我们引入了ResQu，这是一种新颖的SR框架，它将四元数小波预处理框架与潜在扩散模型相结合，并引入了一种新的四元数小波和时间感知编码器。 与之前简单地在扩散模型中应用小波变换的方法不同，我们的方法通过利用四元数小波嵌入来增强条件过程，这些嵌入在去噪的不同阶段动态集成。 此外，我们还利用了基础模型（如Stable Diffusion）的生成先验。 在特定领域数据集上的广泛实验表明，我们的方法实现了卓越的SR结果，在许多情况下，在感知质量和标准评估指标方面优于现有方法。 代码将在修订过程后提供。 显示英文摘要

摘要： Image Super-Resolution is a fundamental problem in computer vision with broad applications spacing from medical imaging to satellite analysis. The ability to reconstruct high-resolution images from low-resolution inputs is crucial for enhancing downstream tasks such as object detection and segmentation. While deep learning has significantly advanced SR, achieving high-quality reconstructions with fine-grained details and realistic textures remains challenging, particularly at high upscaling factors. Recent approaches leveraging diffusion models have demonstrated promising results, yet they often struggle to balance perceptual quality with structural fidelity. In this work, we introduce ResQu a novel SR framework that integrates a quaternion wavelet preprocessing framework with latent diffusion models, incorporating a new quaternion wavelet- and time-aware encoder. Unlike prior methods that simply apply wavelet transforms within diffusion models, our approach enhances the conditioning process by exploiting quaternion wavelet embeddings, which are dynamically integrated at different stages of denoising. Furthermore, we also leverage the generative priors of foundation models such as Stable Diffusion. Extensive experiments on domain-specific datasets demonstrate that our method achieves outstanding SR results, outperforming in many cases existing approaches in perceptual quality and standard evaluation metrics. The code will be available after the revision process.