Image resolution is an important criterion for many applications based on satellite imagery. In this work, we adapt a state-of-the-art kernel regression technique for smartphone camera burst super-resolution to satellites. This technique leverages the local structure of the image to optimally steer the fusion kernels, limiting blur in the final high-resolution prediction, denoising the image, and recovering details up to a zoom factor of 2. We extend this approach to the multi-exposure case to predict from a sequence of multi-exposure low-resolution frames a high-resolution and noise-free one. Experiments on both single and multi-exposure scenarios show the merits of the approach. Since the fusion is learning-free, the proposed method is ensured to not hallucinate details, which is crucial for many remote sensing applications.

翻译：图像分辨率是基于卫星影像的许多应用中的重要标准。在本研究中，我们将一种用于智能手机相机突发超分辨率的最新核回归技术适配至卫星领域。该技术利用图像的局部结构来优化引导融合核，从而限制最终高分辨率预测中的模糊，对图像进行去噪，并恢复高达两倍放大倍率的细节。我们将该方法扩展至多曝光情况，以从一系列多曝光低分辨率帧中预测出高分辨率且无噪声的图像。针对单曝光和多曝光场景的实验均展示了该方法的优势。由于融合过程无需学习，所提出的方法确保不会产生细节幻觉，这对于许多遥感应用至关重要。