Ill-posed image reconstruction problems appear in many scenarios such as remote sensing, where obtaining high quality images is crucial for environmental monitoring, disaster management and urban planning. Deep learning has seen great success in overcoming the limitations of traditional methods. However, these inverse problems rarely come with ground truth data, highlighting the importance of unsupervised learning from partial and noisy measurements alone. We propose perspective-equivariant imaging (EI), a framework that leverages perspective variability in optical camera-based imaging systems, such as satellites or handheld cameras, to recover information lost in ill-posed optical camera imaging problems. This extends previous EI work to include a much richer non-linear class of group transforms and is shown to be an excellent prior for satellite and urban image data, where perspective-EI achieves state-of-the-art results in multispectral pansharpening, outperforming other unsupervised methods in the literature. Code at https://andrewwango.github.io/perspective-equivariant-imaging

翻译：病态图像重建问题出现在诸多场景中，例如遥感领域，其中获取高质量图像对环境监测、灾害管理和城市规划至关重要。深度学习中克服了传统方法局限性取得了显著成功。然而，这些逆问题往往缺乏真值数据，凸显了仅从部分噪声测量中进行无监督学习的重要性。我们提出透视等变成像（EI）框架，该框架利用基于光学相机的成像系统（如卫星或手持相机）中的透视变异性，以恢复病态光学相机成像问题中丢失的信息。这扩展了先前EI工作，纳入了更丰富的非线性群变换类别，并被证明是卫星和城市图像数据的优秀先验，其中透视EI在多光谱全色锐化中实现了最先进的结果，优于文献中的其他无监督方法。代码详见https://andrewwango.github.io/perspective-equivariant-imaging