探索基于结构边缘引导的文本到图像扩散模型在多光谱卫星图像修复中的能力
Exploring the Capability of Text-to-Image Diffusion Models with Structural Edge Guidance for Multi-Spectral Satellite Image Inpainting
AI总结：
本文提出基于StableDiffusion和ControlNet的修复框架及RGB到MSI转换方法，发现自监督内部修复在卫星图像修复中质量更高。
摘要：
该文研究了文本到图像修复模型在卫星图像数据中的实用性。针对将结构引导信号注入生成过程以及将修复后的RGB像素转换为更广泛的多光谱（MSI）波段这两个技术挑战，提出了一种基于StableDiffusion和ControlNet的新型修复框架，以及一种RGB到MSI转换的新方法。在更广泛数据集上的结果表明，通过StableDiffusion合成的修复结果存在不期望的伪影，而一种简单的自监督内部修复替代方案实现了更高的合成质量。
英文摘要：
The letter investigates the utility of text-to-image inpainting models for satellite image data. Two technical challenges of injecting structural guiding signals into the generative process as well as translating the inpainted RGB pixels to a wider set of MSI bands are addressed by introducing a novel inpainting framework based on StableDiffusion and ControlNet as well as a novel method for RGB-to-MSI translation. The results on a wider set of data suggest that the inpainting synthesized via StableDiffusion suffers from undesired artifacts and that a simple alternative of self-supervised internal inpainting achieves a higher quality of synthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
