Jitter-Aware Restoration With Equivalent Jitter Model for Remote Sensing Push-Broom Image

Push-broom imaging systems, including linear array (LA) and time delay integration (TDI) sensors, are extensively used in remote sensing for high-resolution image acquisition with continuous spatial coverage. However, platform-induced jitter introduces significant distortions and blurring, especially in TDI systems with multiple integration stages, where the cumulative effects of jitter are more pronounced. Traditional jitter models often struggle to accurately simulate these effects in the presence of measurement noise, hindering effective image restoration. In this article, we propose a novel jitter-aware restoration framework that addresses these challenges in both LA and TDI push-broom imaging systems. Central to our approach is the introduction of an equivalent jitter model (EJM) that is robust to measurement noise. By averaging time-shifted jitter curves across multiple integration stages, the EJM effectively smooths out noise-induced fluctuations, providing a reliable characterization of jitter effects. Leveraging this model, we develop a jitter-aware restoration network (JARNet), a two-stage restoration network that combines optical flow correction (OFC) with spatial-frequency residual learning to mitigate geometric distortions and motion blur. We also design a custom data synthesis pipeline to generate realistic jitter-degraded datasets, facilitating effective training of the network. Experimental results on both synthetic LA and TDI datasets demonstrate that JARNet outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and gradient magnitude similarity deviation (GMSD) metrics. Our framework offers a robust solution for restoring high-quality remote sensing images degraded by jitter, significantly advancing the state-of-the-art in this domain. The source code will be made publicly available upon publication at https://github.com/naturezhanghn/EJM.