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For low-level computer vision and image processing ML tasks, training onlarge datasets is critical for generalization. However, the standard practiceof relying on real-world images primarily from the Internet comes with imagequality, scalability, and privacy issues, especially in commercial contexts. Toaddress this, we have developed a procedural synthetic data generation pipelineand dataset tailored to low-level vision tasks. Our Unreal engine-basedsynthetic data pipeline populates large scenes algorithmically with acombination 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 andvalidated a CNN-based denoising model, and demonstrated that the model trainedon this synthetic data alone can achieve competitive denoising results whenevaluated on real-world noisy images captured with smartphone cameras.

FSID: Fully Synthetic Image Denoising via Procedural Scene Generation