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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. In this work, a real\u2010time, memory\u2010efficient, and reliable noise source estimator that combines data\u2010based and physically based models is investigated. To this end, a deep neural network 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\u2010world noise from two camera systems, and real\u2010field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. 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