论文标题
MUS2：Sentinel-2多图像超级分辨率的现实世界基准
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
论文作者
论文摘要
在许多实际情况下，图像空间分辨率不足是一个严重的局限性，尤其是在以较小规模获取图像的情况下是不可行的或带来更高的成本。这是遥感固有的，包括以高重新访问频率免费免费获得的Sentinel-2卫星图像，但其空间分辨率限制为10 m地面采样距离。可以通过超分辨率算法提高分辨率，特别是当从随后在卫星的后续重访中捕获的多个图像执行时，可以利用信息融合，从而提高重建精度。多图像超分辨率的障碍之一是现实世界基准的稀缺性 - 通常利用模拟数据，并不能完全反映工作条件。在本文中，我们介绍了一种新的MUS2基准，用于超出多个Sentinel-2图像，并将Worldview-2图像用作高分辨率参考。在MUS2中，我们针对此问题发布了第一个端到端评估程序，我们期望帮助研究人员在多图像超分辨率方面推进最新技术。
Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is limited to 10 m ground sampling distance. The resolution can be increased with super-resolution algorithms, in particular when performed from multiple images captured at subsequent revisits of a satellite, taking advantage of information fusion that leads to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-world benchmarks - commonly, simulated data are exploited which do not fully reflect the operating conditions. In this paper, we introduce a new MuS2 benchmark for super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the first end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image super-resolution.
