用AI自适应调整相机参数，让普通手机拍出专业级照片。
From Cheap to Pro: A Learning-based Adaptive Camera Parameter Network for Professional-Style Imaging
- 从RAW图像直接预测曝光与白平衡参数，实现端到端优化。
- 在弱光、逆光等复杂光照下显著减少过暗、偏色和明暗不均。
- 轻量设计适合边缘设备，无需额外增强模块也能提升画质。
消费级相机系统在复杂光照条件（如低光、高动态范围、逆光及空间色温变化）下常面临图像质量不稳定的问题，导致欠曝、色偏和明暗不均，进而影响下游视觉任务表现。为此，我们提出ACamera-Net，一个轻量级且场景自适应的相机参数调节网络，可直接从RAW输入预测最优曝光与白平衡参数。该框架包含两个模块：ACamera-Exposure用于估计ISO以缓解欠曝与对比度损失；ACamera-Color则预测相关色温与增益因子，提升色彩一致性。模型针对边缘设备实时推理优化，可无缝集成至成像流程。在包含标注参考图像的多样化真实数据上训练，具备良好跨光照条件泛化能力。大量实验表明，ACamera-Net持续提升图像质量并稳定感知输出，在性能上优于传统自动模式及轻量基线，且无需依赖额外图像增强模块。
原文摘要 · Abstract (English)
Consumer-grade camera systems often struggle to maintain stable image quality under complex illumination conditions such as low light, high dynamic range, and backlighting, as well as spatial color temperature variation. These issues lead to underexposure, color casts, and tonal inconsistency, which degrade the performance of downstream vision tasks. To address this, we propose ACamera-Net, a lightweight and scene-adaptive camera parameter adjustment network that directly predicts optimal exposure and white balance from RAW inputs. The framework consists of two modules: ACamera-Exposure, which estimates ISO to alleviate underexposure and contrast loss, and ACamera-Color, which predicts correlated color temperature and gain factors for improved color consistency. Optimized for real-time inference on edge devices, ACamera-Net can be seamlessly integrated into imaging pipelines. Trained on diverse real-world data with annotated references, the model generalizes well across lighting conditions. Extensive experiments demonstrate that ACamera-Net consistently enhances image quality and stabilizes perception outputs, outperforming conventional auto modes and lightweight baselines without relying on additional image enhancement modules.
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