Due to atmospheric turbulence, optical system limitations, satellite platform jitter, and other reasons, remote-sensing images inevitably undergo different degrees of degradation. Employing the deep-learning method to improve the on-orbit image quality faces many challenges such as lack of data, limited computing resources, network architecture design, and so on. Among these factors, establishing a physics-guided dataset during the image restoration stage and avoiding unforeseen effects such as ringing pose a significant challenge for remote-sensing image restoration. This letter proposes an optical imaging degradation simulation model and transformer-based algorithm to improve remote-sensing image quality. First, we model the degradation result from phase to image of optical remote-sensing imaging using Zernike polynomials, thus, a large-scale paired dataset is constructed. Then, a multilevel feature fusion transformer (MFFormer) is introduced to mitigate the defect during restoration. The proposed algorithm incorporates a multilevel feature fusion (MFF) module to fuse feature information from multiscales effectively. Additionally, a multilevel space and frequency loss function is introduced to enhance the learning of high-frequency information to ensure that the edge suppresses noise amplification and ringing effects during recovery. Finally, experimental results on synthetic data show that our method improved by 25.4% and 22.3% with the blurred images on the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) index. Visual results on the GaoFen-1/2A PMS images have enhanced clarity and suppressed artifacts such as ringing which demonstrate the effectiveness and capability of our proposed method.

中文翻译：

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遥感光学成像退化模拟和基于变压器的图像恢复

由于大气湍流、光学系统限制、卫星平台抖动等原因，遥感图像不可避免地会出现不同程度的退化。利用深度学习方法提高在轨图像质量面临着数据缺乏、计算资源有限、网络架构设计等诸多挑战。其中，在图像恢复阶段建立物理引导的数据集并避免振铃等不可预见的影响对遥感图像恢复提出了重大挑战。这封信提出了一种光学成像退化模拟模型和基于变压器的算法来提高遥感图像质量。首先，我们使用泽尼克多项式对光学遥感成像从相位到图像的退化结果进行建模，从而构建了大规模的配对数据集。然后，引入多级特征融合变压器（MFFormer）来减轻恢复过程中的缺陷。该算法结合了多级特征融合（MFF）模块来有效地融合多尺度的特征信息。此外，引入多级空间和频率损失函数来增强高频信息的学习，以确保边缘在恢复过程中抑制噪声放大和振铃效应。最后，合成数据的实验结果表明，我们的方法在峰值信噪比（PSNR）和结构相似性（SSIM）指标上与模糊图像相比分别提高了 25.4% 和 22.3%。 GauFen-1/2A PMS 图像的视觉结果提高了清晰度并抑制了振铃等伪影，这证明了我们提出的方法的有效性和能力。