Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912641669136384 |
|---|---|
| author | Zhu, Qiang Jiang, Yuxuan Zhu, Shuyuan Zhang, Fan Bull, David Zeng, Bing |
| author_facet | Zhu, Qiang Jiang, Yuxuan Zhu, Shuyuan Zhang, Fan Bull, David Zeng, Bing |
| contents | Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blind Video Super-Resolution based on Implicit Kernels Zhu, Qiang Jiang, Yuxuan Zhu, Shuyuan Zhang, Fan Bull, David Zeng, Bing Computer Vision and Pattern Recognition Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK. |
| title | Blind Video Super-Resolution based on Implicit Kernels |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07856 |