Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916801839890432 |
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| author | Brummer, Benoit De Vleeschouwer, Christophe |
| author_facet | Brummer, Benoit De Vleeschouwer, Christophe |
| contents | This paper introduces the Raw Natural Image Noise Dataset (RawNIND), a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles. Two denoising methods are proposed: one operates directly on raw Bayer data, leveraging computational efficiency, while the other processes linear RGB images for improved generalization to different sensors, with both preserving flexibility for subsequent development. Both methods outperform traditional approaches which rely on developed images. Additionally, the integration of denoising and compression at the raw data level significantly enhances rate-distortion performance and computational efficiency. These findings suggest a paradigm shift toward raw data workflows for efficient and flexible image processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08924 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset Brummer, Benoit De Vleeschouwer, Christophe Computer Vision and Pattern Recognition Image and Video Processing 68U10 I.4.2; I.4.3; I.4.4; I.4.9; I.2.10; H.4.3 This paper introduces the Raw Natural Image Noise Dataset (RawNIND), a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles. Two denoising methods are proposed: one operates directly on raw Bayer data, leveraging computational efficiency, while the other processes linear RGB images for improved generalization to different sensors, with both preserving flexibility for subsequent development. Both methods outperform traditional approaches which rely on developed images. Additionally, the integration of denoising and compression at the raw data level significantly enhances rate-distortion performance and computational efficiency. These findings suggest a paradigm shift toward raw data workflows for efficient and flexible image processing. |
| title | Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset |
| topic | Computer Vision and Pattern Recognition Image and Video Processing 68U10 I.4.2; I.4.3; I.4.4; I.4.9; I.2.10; H.4.3 |
| url | https://arxiv.org/abs/2501.08924 |