边端设备上联合去噪与分类，提升低光图像可读性。
Pushing Joint Image Denoising and Classification to the Edge
- 设计融合去噪与分类的轻量级架构，适配边缘设备算力。
- 通过优化延迟、准确率和去噪效果，实现性能超越人工设计。
- 适合医疗影像、安防监控等对图像感知要求高的场景。
本文联合图像分类与图像去噪任务，旨在提升由边缘设备（如低光照安全摄像头）捕获的噪声图像中的人类感知能力。在该场景下，保持人类对自动分类结果的可验证性至关重要，因此需联合去噪以增强视觉可读性。由于边缘设备计算资源有限，本文提出一种新架构，显式优化效率。同时，改进神经架构搜索（NAS）方法，在目标延迟、分类准确率和去噪性能之间进行联合搜索，以找到最优集成模型。实验表明，该方法生成的NAS架构在去噪与分类性能上均优于人工设计的基准模型，显著改善了人类对图像的感知体验。该方法可应用于医学影像、监控系统及工业检测等需要高感知质量的领域。
原文摘要 · Abstract (English)
In this paper, we jointly combine image classification and image denoising, aiming to enhance human perception of noisy images captured by edge devices, like low-light security cameras. In such settings, it is important to retain the ability of humans to verify the automatic classification decision and thus jointly denoise the image to enhance human perception. Since edge devices have little computational power, we explicitly optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural Architecture Search (NAS) method, which searches for classifiers to search for the integrated model while optimizing for a target latency, classification accuracy, and denoising performance. The NAS architectures outperform our manually designed alternatives in both denoising and classification, offering a significant improvement to human perception. Our approach empowers users to construct architectures tailored to domains like medical imaging, surveillance systems, and industrial inspections.
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