Progressive Learning-Based Jitter Distortion Correction for Remote Sensing Images of Time Delay and Integration Camera

The widespread use of time delay and integration charge-coupled device (TDI CCD) technology in high-resolution spaceborne optical cameras has made high-frequency jitter effects a common issue, resulting in different levels of distortion in images. Current methods mostly concentrate on correction of obviously high levels of geometric distortion. Focusing on low levels of geometric distortion, which are more difficult to accurately detect, this paper proposes a progressive learning-based correction method for high-frequency jitter distortion in remote sensing images from spaceborne TDI CCD cameras, utilizing a Generative Adversarial Network (GAN). First, a distorted dataset with diverse jitter levels for progressive training is generated through jitter simulation model by adjusting the parameters. Then, a GAN model is employed for the correction task. The generator consists of the Distortion Net for geometric distortion correction and the Detail Enhancement Net for image detail restoration. Finally, a progressive learning strategy is used to gradually enhance the ability of network to correct minor geometric distortion. The proposed method is validated using simulated images and real-world satellite images. Experimental results demonstrate that the proposed method outperforms existing restoration methods both in simulated datasets and practical scenarios.