Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909601691074560 |
|---|---|
| author | Lu, Yi Wang, Yadong Jiang, Xingbo Bai, Xiangzhi |
| author_facet | Lu, Yi Wang, Yadong Jiang, Xingbo Bai, Xiangzhi |
| contents | Infrared images captured under turbulent conditions are degraded by complex geometric distortions and blur. We address infrared deturbulence as an image restoration task, proposing DparNet, a parameter-assisted multi-frame network with a wide & deep architecture. DparNet learns a degradation prior (key parameter matrix) directly from degraded images without external knowledge. Its wide & deep architecture uses these learned parameters to directly modulate restoration, achieving spatially and intensity adaptive results. Evaluated on dedicated infrared deturbulence (49,744 images) and visible image denoising (109,536 images) datasets, DparNet significantly outperforms State-of-the-Art (SOTA) methods in restoration performance and efficiency. Notably, leveraging these parameters improves PSNR by 0.6-1.1 dB with less than 2% increase in model parameters and computational complexity. Our work demonstrates that degraded images hide key degradation information that can be learned and utilized to boost adaptive image restoration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_18708 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning Lu, Yi Wang, Yadong Jiang, Xingbo Bai, Xiangzhi Computer Vision and Pattern Recognition Image and Video Processing Infrared images captured under turbulent conditions are degraded by complex geometric distortions and blur. We address infrared deturbulence as an image restoration task, proposing DparNet, a parameter-assisted multi-frame network with a wide & deep architecture. DparNet learns a degradation prior (key parameter matrix) directly from degraded images without external knowledge. Its wide & deep architecture uses these learned parameters to directly modulate restoration, achieving spatially and intensity adaptive results. Evaluated on dedicated infrared deturbulence (49,744 images) and visible image denoising (109,536 images) datasets, DparNet significantly outperforms State-of-the-Art (SOTA) methods in restoration performance and efficiency. Notably, leveraging these parameters improves PSNR by 0.6-1.1 dB with less than 2% increase in model parameters and computational complexity. Our work demonstrates that degraded images hide key degradation information that can be learned and utilized to boost adaptive image restoration. |
| title | Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2305.18708 |