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- W4310997667 abstract "For low-level computer vision and image processing ML tasks, training on large datasets is critical for generalization. However, the standard practice of relying on real-world images primarily from the Internet comes with image quality, scalability, and privacy issues, especially in commercial contexts. To address this, we have developed a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks. Our Unreal engine-based synthetic data pipeline populates large scenes algorithmically with a combination of random 3D objects, materials, and geometric transformations. Then, we calibrate the camera noise profiles to synthesize the noisy images. From this pipeline, we generated a fully synthetic image denoising dataset (FSID) which consists of 175,000 noisy/clean image pairs. We then trained and validated a CNN-based denoising model, and demonstrated that the model trained on this synthetic data alone can achieve competitive denoising results when evaluated on real-world noisy images captured with smartphone cameras." @default.
- W4310997667 created "2022-12-22" @default.
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- W4310997667 date "2022-12-07" @default.
- W4310997667 modified "2023-10-17" @default.
- W4310997667 title "FSID: Fully Synthetic Image Denoising via Procedural Scene Generation" @default.
- W4310997667 doi "https://doi.org/10.48550/arxiv.2212.03961" @default.
- W4310997667 hasPublicationYear "2022" @default.
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