Abstract
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.
Abstract (translated)
消费者级相机系统在复杂光照条件下（如低光、高动态范围和逆光）以及空间色温变化的情况下，往往难以维持稳定的图像质量。这些问题导致了曝光不足、色调偏差和色彩不一致，从而降低了下游视觉任务的表现。为了解决这一问题，我们提出了ACamera-Net，这是一种轻量级且场景适应性强的相机参数调整网络，可以直接从RAW输入预测最佳的曝光度和白平衡设置。该框架由两个模块组成：ACamera-Exposure用于估计ISO值以减轻曝光不足和对比度损失；ACamera-Color则预测相关色温及增益因子，从而提升色彩一致性。针对边缘设备上的实时推理进行了优化，ACamera-Net可以无缝集成到成像流水线中。通过使用带有注释参考的多样化真实世界数据进行训练，该模型在各种光照条件下表现良好且具有泛化能力。广泛的实验表明，与传统自动模式和轻量级基线相比，ACamera-Net在无需额外图像增强模块的情况下持续改善了图像质量和稳定了感知输出，并表现出色。
URL
https://arxiv.org/abs/2510.20550
