Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset

• URL: http://arxiv.org/abs/2501.08924v1
• Date: Wed, 15 Jan 2025 16:30:05 GMT
• Title: Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset
• Authors: Benoit Brummer, Christophe De Vleeschouwer,
• Abstract summary: This paper introduces the Raw Natural Image Noise dataset (RawNIND) RawNIND is a diverse collection of raw images designed to support the development of denoising models that generalize across sensors, image development paired, 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 to different sensors.
• Score: 19.773845221148015
• License:
• Abstract: 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.

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