We introduce an innovative deep learning-based method that uses a denoising diffusion-based model to translate low-resolution images to high-resolution ones from different optical sensors while preserving the contents and avoiding undesired artifacts. The proposed method is trained and tested on a large and diverse data set of paired Sentinel-II and Planet Dove images. We show that it can solve serious image generation issues observed when the popular classifier-free guided Denoising Diffusion Implicit Model (DDIM) framework is used in the task of Image-to-Image Translation of multi-sensor optical remote sensing images and that it can generate large images with highly consistent patches, both in colors and in features. Moreover, we demonstrate how our method improves heterogeneous change detection results in two urban areas: Beirut, Lebanon, and Austin, USA. Our contributions are: i) a new training and testing algorithm based on denoising diffusion models for optical image translation; ii) a comprehensive image quality evaluation and ablation study; iii) a comparison with the classifier-free guided DDIM framework; and iv) change detection experiments on heterogeneous data.

翻译：我们提出了一种创新的深度学习方法，该方法采用基于去噪扩散的模型，将来自不同光学传感器的低分辨率图像转换为高分辨率图像，同时保留内容并避免产生伪影。该方法在包含配对的 Sentinel-II 和 Planet Dove 图像的大规模多样化数据集上进行了训练和测试。我们证明了该方法能够解决在将流行的无分类器引导的去噪扩散隐式模型（DDIM）框架用于多传感器光学遥感图像到图像翻译时出现的严重图像生成问题，并且能够生成颜色和特征都高度一致的大尺寸图像块。此外，我们展示了该方法如何改善两个城市区域（黎巴嫩贝鲁特和美国奥斯汀）的异构变化检测结果。本文的贡献包括：i) 一种基于去噪扩散模型用于光学图像翻译的新型训练与测试算法；ii) 全面的图像质量评估与消融研究；iii) 与无分类器引导的 DDIM 框架的对比；iv) 在异构数据上的变化检测实验。