Technology Category
Computer Vision: Learning & Optimization for CVMachine Learning: Learning on the Edge & Model CompressionSearch and Optimization: Non-convex Optimization
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset
🤖 AI Summary
Real-world sensor noise exhibits high diversity, making it challenging to jointly optimize raw-image denoising, detail recovery, and compression. Method: We introduce RawNIND—the first paired raw-image dataset—and propose two end-to-end joint denoising-and-compression frameworks operating directly in the raw domain: a Bayer-domain method for computational efficiency and a linear RGB-domain method for cross-sensor generalizability. Our approach unifies denoising, demosaicking, and compression within a single differentiable pipeline, adopting a dual-input-stream deep architecture with rate-distortion-optimized coding. Contribution/Results: This work establishes the “raw-data-first” paradigm for image processing. Experiments demonstrate consistent superiority over sRGB-domain baselines across cross-sensor generalization, PSNR/SSIM, and rate-distortion performance; encoding efficiency improves significantly, and inference speed increases by 40%.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Image Restoration
Noise Reduction
File Size Optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
RawNIND Dataset
Simultaneous Denoising and Compression
Efficiency Improvement
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2024-01-24arXiv.orgCitations: 0
