MicroZoom：极端尺度下的结构保留细节合成
MicroZoom: Structure-Preserving Detail Synthesis at Extreme Scale
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中文总结 AI 辅助
MicroZoom旨在解决微观尺度下基于参考的极端尺度超分辨率问题，通过两阶段级联设计，第一阶段恢复全局图案连贯性，第二阶段细化局部纹理细节，辅以分割掩码指导合成，实现了全局连贯、基于物质的千兆像素图像合成。
中文摘要 AI 辅助
我们介绍了MicroZoom，这是一个用于微观尺度下千兆像素图像合成的生成框架。给定一张标准照片和一组稀疏的消费级显微镜特写，MicroZoom能合成出无缝的、基于真实参考资料物质特征的千兆像素分辨率图像，实现对物体整个空间范围微观纹理的探索性可视化。我们的目标是合理合成而非精确重建。我们专注于全图像、基于参考、高达350倍放大倍数的极端尺度超分辨率，此设置带来两个主要挑战：从模糊物质边界附近的高损耗输入中恢复特定纹理细节，以及在数百万个局部预测中保留正确的大规模图案结构。我们通过两阶段级联设计解决这些问题，第一阶段恢复全局图案连贯性，第二阶段细化局部纹理细节，并辅以分割掩码来指导模糊边界处的合成。我们在一组自拍摄的日常物体上验证了我们的方法，并展示了全局连贯、基于物质的千兆像素图像。
英文摘要
We introduce MicroZoom, a generative framework for gigapixel image synthesis at the microscopic scale. Given a standard photograph and a sparse set of consumer-grade microscope close-ups, MicroZoom synthesizes a seamless, gigapixel-resolution image grounded in the material character of the real references, enabling exploratory visualization of microscopic texture across the full spatial extent of an object. Our goal is plausible synthesis, not exact reconstruction. We focus on full-image, reference-based, extreme-scale super-resolution at magnification levels of up to 350x, a setting that introduces two major challenges: (1) recovering texture-specific detail from highly lossy inputs near ambiguous material boundaries, and (2) preserving correct large-scale pattern structure, such as the repeating geometry of a fabric weave, across millions of local predictions. We address these with a two-stage cascaded design, where the first stage recovers global pattern coherence and the second refines local texture detail, supplemented by a segmentation mask to guide synthesis at ambiguous boundaries. We verify our approach on a collection of self-captured everyday objects and demonstrate globally coherent, materially grounded gigapixel imagery.
发表机构
- University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。
