Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline
  and Jitter-Aware Restoration Network
Zida Chen Ziran Zhang Haoying Li Menghao Li Yueting Chen Qi Li Huajun Feng Zhihai Xu Shiqi Chen
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.