Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914175214682112 |
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| author | Huang, Shaoguang Wang, Yunzhen Zeng, Haijin Chen, Hongyu Zhang, Hongyan |
| author_facet | Huang, Shaoguang Wang, Yunzhen Zeng, Haijin Chen, Hongyu Zhang, Hongyan |
| contents | Snapshot compressive imaging (SCI) captures multispectral images (MSIs) using a single coded two-dimensional (2-D) measurement, but reconstructing high-fidelity MSIs from these compressed inputs remains a fundamentally ill-posed challenge. While diffusion-based reconstruction methods have recently raised the bar for quality, they face critical limitations: a lack of large-scale MSI training data, adverse domain shifts from RGB-pretrained models, and inference inefficiencies due to multi-step sampling. These drawbacks restrict their practicality in real-world applications. In contrast to existing methods, which either follow costly iterative refinement or adapt subspace-based embeddings for diffusion models (e.g. DiffSCI, PSR-SCI), we introduce a fundamentally different paradigm: a self-supervised One-Step Diffusion (OSD) framework specifically designed for SCI. The key novelty lies in using a single-step diffusion refiner to correct an initial reconstruction, eliminating iterative denoising entirely while preserving generative quality. Moreover, we adopt a self-supervised equivariant learning strategy to train both the predictor and refiner directly from raw 2-D measurements, enabling generalization to unseen domains without the need for ground-truth MSI. To further address the challenge of limited MSI data, we design a band-selection-driven distillation strategy that transfers core generative priors from large-scale RGB datasets, effectively bridging the domain gap. Extensive experiments confirm that our approach sets a new benchmark, yielding PSNR gains of 3.44 dB, 1.61 dB, and 0.28 dB on the Harvard, NTIRE, and ICVL datasets, respectively, while reducing reconstruction time by 97.5%. This remarkable improvement in efficiency and adaptability makes our method a significant advancement in SCI reconstruction, combining both accuracy and practicality for real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07417 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging Huang, Shaoguang Wang, Yunzhen Zeng, Haijin Chen, Hongyu Zhang, Hongyan Image and Video Processing Computer Vision and Pattern Recognition Snapshot compressive imaging (SCI) captures multispectral images (MSIs) using a single coded two-dimensional (2-D) measurement, but reconstructing high-fidelity MSIs from these compressed inputs remains a fundamentally ill-posed challenge. While diffusion-based reconstruction methods have recently raised the bar for quality, they face critical limitations: a lack of large-scale MSI training data, adverse domain shifts from RGB-pretrained models, and inference inefficiencies due to multi-step sampling. These drawbacks restrict their practicality in real-world applications. In contrast to existing methods, which either follow costly iterative refinement or adapt subspace-based embeddings for diffusion models (e.g. DiffSCI, PSR-SCI), we introduce a fundamentally different paradigm: a self-supervised One-Step Diffusion (OSD) framework specifically designed for SCI. The key novelty lies in using a single-step diffusion refiner to correct an initial reconstruction, eliminating iterative denoising entirely while preserving generative quality. Moreover, we adopt a self-supervised equivariant learning strategy to train both the predictor and refiner directly from raw 2-D measurements, enabling generalization to unseen domains without the need for ground-truth MSI. To further address the challenge of limited MSI data, we design a band-selection-driven distillation strategy that transfers core generative priors from large-scale RGB datasets, effectively bridging the domain gap. Extensive experiments confirm that our approach sets a new benchmark, yielding PSNR gains of 3.44 dB, 1.61 dB, and 0.28 dB on the Harvard, NTIRE, and ICVL datasets, respectively, while reducing reconstruction time by 97.5%. This remarkable improvement in efficiency and adaptability makes our method a significant advancement in SCI reconstruction, combining both accuracy and practicality for real-world deployment. |
| title | Self-Supervised One-Step Diffusion Refinement for Snapshot Compressive Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07417 |