Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availability of paired datasets is a strong assumption. Consequently, many unsupervised algorithms have been proposed to address this problem. These methods synthetically generate a large dataset of degraded images using image formation models. A neural network is then trained with an adversarial loss to discriminate between images from distorted and clean domains. However, these methods yield suboptimal performance when tested on real images that do not necessarily conform to the generation mechanism. Also, they require a large amount of training data and are rendered unsuitable when only a few images are available. We propose a distortion disentanglement and knowledge distillation framework for satellite image restoration to address these important issues. Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics. Specifically, we first propose a mechanism to disentangle distortion. This enables us to generate images with varying degrees of distortion using the disentangled distortion and the reference image. We then propose the use of knowledge distillation to train a restoration network using the generated image pairs. As a final step, the distorted image is passed through the restoration network to get the final output. Ablation studies show that our proposed mechanism successfully disentangles distortion.

翻译：卫星图像通常受到多种失真影响。大气变化、地表反射率、太阳光照及观测几何等因素均会影响卫星图像质量，从而限制其在下游任务中的应用。在有监督网络中，成对数据集的可用性是一个强假设条件。为此，研究者提出了许多无监督算法来解决该问题。这些方法利用图像形成模型合成生成大规模退化图像数据集，随后通过对抗性损失训练神经网络以区分失真域与干净域的图像。然而，当测试真实图像时，这些方法因真实图像未必符合生成机制而表现欠佳。此外，它们需要大量训练数据，在仅有少量可用图像时难以适用。针对这些关键问题，我们提出了一种基于失真解耦与知识蒸馏的卫星图像恢复框架。该算法仅需两张图像：待恢复的失真卫星图像和具有相似语义的参考图像。具体而言，我们首先提出失真解耦机制，利用解耦后的失真与参考图像生成具有不同程度失真的图像。随后引入知识蒸馏方法，利用生成的图像对训练恢复网络。最后将失真图像输入恢复网络获得最终输出。消融实验表明，我们提出的机制能够有效实现失真解耦。