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Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network

16 Jan 2024arXiv:2401.08171archive 2025-07-28

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.

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flow_warp JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository ran · our draft was wrong Apache-2.0 (permissive) · da089fa07c3ea836 · report
window_partitions JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · fc1fb34133fbe733 · report
window_partitionx JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · 0237e2c8c671688e · report
window_reverses JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 93aeadff775de009 · report
window_reversex JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository ran Apache-2.0 (permissive) · f7c51e5750d02638 · report
JARNet JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository unverified Apache-2.0 (permissive) · 7fc1e84e19fc8d23 · report
SFResBlock JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository unverified Apache-2.0 (permissive) · cdf06b13ce7688ca · report
fft_bench_complex_mlp JHW2000/JARNet/basicsr/models/archs/jarnet_arch.py official repository unverified Apache-2.0 (permissive) · 78848daf166ca48a · report

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Image GenerationImage RestorationOptical Flow Estimation

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