Authors: Omar Elezabi, Marcos V. Conde, Radu Timofte

Published on: April 17, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2404.11569

Summary

• What is new: A novel module that captures global context information from full RAW images, integrated into a neural Image Signal Processor (ISP).
• Why this is important: Existing deep learning-based ISPs are limited by training on patches, missing global context, affecting image quality.
• What the research proposes: An efficient neural ISP leveraging a new module to understand and incorporate global context, enhancing image processing.
• Results: The proposed ISP model achieves state-of-the-art results on various benchmarks with diverse, real smartphone images.

Technical Details

Technological frameworks used: Deep learning-based image processing

Models used: Neural ISPs incorporating a novel global context module

Data used: Diverse and real smartphone RAW images

Potential Impact

Smartphone manufacturers, camera app developers, and companies in the photography and imaging technology sectors.

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