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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00161 |
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| _version_ | 1866914088741765120 |
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| author | Stathatos, Suzanne Hobley, Michael Perona, Pietro Marks, Markus |
| author_facet | Stathatos, Suzanne Hobley, Michael Perona, Pietro Marks, Markus |
| contents | Low signal-to-noise ratio videos -- such as those from underwater sonar, ultrasound, and microscopy -- pose significant challenges for computer vision models, particularly when paired clean imagery is unavailable. We present Spatiotemporal Augmentations and denoising in Video for Downstream Tasks (SAVeD), a novel self-supervised method that denoises low-SNR sensor videos using only raw noisy data. By leveraging distinctions between foreground and background motion and exaggerating objects with stronger motion signal, SAVeD enhances foreground object visibility and reduces background and camera noise without requiring clean video. SAVeD has a set of architectural optimizations that lead to faster throughput, training, and inference than existing deep learning methods. We also introduce a new denoising metric, FBD, which indicates foreground-background divergence for detection datasets without requiring clean imagery. Our approach achieves state-of-the-art results for classification, detection, tracking, and counting tasks, and it does so with fewer training resource requirements than existing deep-learning-based denoising methods. Project page: https://suzanne-stathatos.github.io/SAVeD Code page: https://github.com/suzanne-stathatos/SAVeD |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance Stathatos, Suzanne Hobley, Michael Perona, Pietro Marks, Markus Computer Vision and Pattern Recognition Low signal-to-noise ratio videos -- such as those from underwater sonar, ultrasound, and microscopy -- pose significant challenges for computer vision models, particularly when paired clean imagery is unavailable. We present Spatiotemporal Augmentations and denoising in Video for Downstream Tasks (SAVeD), a novel self-supervised method that denoises low-SNR sensor videos using only raw noisy data. By leveraging distinctions between foreground and background motion and exaggerating objects with stronger motion signal, SAVeD enhances foreground object visibility and reduces background and camera noise without requiring clean video. SAVeD has a set of architectural optimizations that lead to faster throughput, training, and inference than existing deep learning methods. We also introduce a new denoising metric, FBD, which indicates foreground-background divergence for detection datasets without requiring clean imagery. Our approach achieves state-of-the-art results for classification, detection, tracking, and counting tasks, and it does so with fewer training resource requirements than existing deep-learning-based denoising methods. Project page: https://suzanne-stathatos.github.io/SAVeD Code page: https://github.com/suzanne-stathatos/SAVeD |
| title | SAVeD: Learning to Denoise Low-SNR Video for Improved Downstream Performance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.00161 |