Autonomous machines must self-maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. This work investigates a real-time, memory-efficient and reliable noise source estimator that combines data- and physically-based models. To this end, a DNN that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real-world noise from two camera systems, and real field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug-and-play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback-loop to approach fully reliable machines.

自主机器必须自我维持适当功能以确保人类和自身的安全，本研究主要研究了一个实时、内存高效和可靠的噪声源估计器，结合了数据和物理模型，用于检测相机的噪声源，通过分析相机元数据和图像来量化噪声的贡献。

基于图像和元数据的相机系统实时噪声源估计