Towards Efficient Image Deblurring for Edge Deployment
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911381962358784 |
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| author | Miriyala, Srinivas Vajrala, Sowmya Kodavanti, Sravanth |
| author_facet | Miriyala, Srinivas Vajrala, Sowmya Kodavanti, Sravanth |
| contents | Image deblurring is a critical stage in mobile image signal processing pipelines, where the ability to restore fine structures and textures must be balanced with real-time constraints on edge devices. While recent deep networks such as transformers and activation-free architectures achieve state-of-the-art (SOTA) accuracy, their efficiency is typically measured in FLOPs or parameters, which do not correlate with latency on embedded hardware. We propose a hardware-aware adaptation framework that restructures existing models through sensitivity-guided block substitution, surrogate distillation, and training-free multi-objective search driven by device profiling. Applied to the 36-block NAFNet baseline, the optimized variants achieve up to 55% reduction in GMACs compared to the recent transformer-based SOTA while maintaining competitive accuracy. Most importantly, on-device deployment yields a 1.25X latency improvement over the baseline. Experiments on motion deblurring (GoPro), defocus deblurring (DPDD), and auxiliary benchmarks (RealBlur-J/R, HIDE) demonstrate the generality of the approach, while comparisons with prior efficient baselines confirm its accuracy-efficiency trade-off. These results establish feedback-driven adaptation as a principled strategy for bridging the gap between algorithmic design and deployment-ready deblurring models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11685 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Efficient Image Deblurring for Edge Deployment Miriyala, Srinivas Vajrala, Sowmya Kodavanti, Sravanth Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Image deblurring is a critical stage in mobile image signal processing pipelines, where the ability to restore fine structures and textures must be balanced with real-time constraints on edge devices. While recent deep networks such as transformers and activation-free architectures achieve state-of-the-art (SOTA) accuracy, their efficiency is typically measured in FLOPs or parameters, which do not correlate with latency on embedded hardware. We propose a hardware-aware adaptation framework that restructures existing models through sensitivity-guided block substitution, surrogate distillation, and training-free multi-objective search driven by device profiling. Applied to the 36-block NAFNet baseline, the optimized variants achieve up to 55% reduction in GMACs compared to the recent transformer-based SOTA while maintaining competitive accuracy. Most importantly, on-device deployment yields a 1.25X latency improvement over the baseline. Experiments on motion deblurring (GoPro), defocus deblurring (DPDD), and auxiliary benchmarks (RealBlur-J/R, HIDE) demonstrate the generality of the approach, while comparisons with prior efficient baselines confirm its accuracy-efficiency trade-off. These results establish feedback-driven adaptation as a principled strategy for bridging the gap between algorithmic design and deployment-ready deblurring models. |
| title | Towards Efficient Image Deblurring for Edge Deployment |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.11685 |