Linear Array Pushbroom (LAP) imaging technology is widely used in the realm of remote sensing. However, images acquired through LAP always suffer from distortion and blur because of camera jitter. Traditional methods for restoring LAP images, such as algorithms estimating the point spread function (PSF), exhibit limited performance. To tackle this issue, we propose a Jitter-Aware Restoration Network (JARNet), to remove the distortion and blur in two stages. In the first stage, we formulate an Optical Flow Correction (OFC) block to refine the optical flow of the degraded LAP images, resulting in pre-corrected images where most of the distortions are alleviated. In the second stage, for further enhancement of the pre-corrected images, we integrate two jitter-aware techniques within the Spatial and Frequency Residual (SFRes) block: 1) introducing Coordinate Attention (CoA) to the SFRes block in order to capture the jitter state in orthogonal direction; 2) manipulating image features in both spatial and frequency domains to leverage local and global priors. Additionally, we develop a data synthesis pipeline, which applies Continue Dynamic Shooting Model (CDSM) to simulate realistic degradation in LAP images. Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge. Extensive experiments demonstrate that the proposed two-stage method outperforms state-of-the-art image restoration models. Code is available at https://github.com/JHW2000/JARNet.

翻译：线阵推扫式（LAP）成像技术广泛应用于遥感领域。然而，由于相机抖动，通过LAP获取的图像常存在畸变和模糊问题。传统LAP图像复原方法（如点扩散函数（PSF）估计算法）性能有限。为解决此问题，我们提出一种抖动感知复原网络（JARNet），通过两阶段去除畸变和模糊。第一阶段，我们构建光流校正（OFC）模块以优化退化LAP图像的光流，生成预校正图像，其中大部分畸变得以缓解。第二阶段，为增强预校正图像，我们在空间与频率残差（SFRes）块中集成两种抖动感知技术：1）在SFRes块中引入坐标注意力（CoA），以捕获正交方向上的抖动状态；2）在空间域和频率域中联合操作图像特征，利用局部和全局先验。此外，我们开发了一种数据合成管线，应用连续动态拍摄模型（CDSM）模拟LAP图像的真实退化。所提出的JARNet与LAP图像合成管线共同为解决这一复杂挑战奠定了基础。大量实验表明，所提出的两阶段方法优于最先进的图像复原模型。代码见：https://github.com/JHW2000/JARNet。