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

2025-01-15

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

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Non-convex Optimization

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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

• Image Restoration
• Noise Reduction
• File Size Optimization

Innovation

• RawNIND Dataset
• Simultaneous Denoising and Compression
• Efficiency Improvement