画像とメタデータからのカメラシステムのリアルタイムノイズ源推定
Real-time Noise Source Estimation of a Camera System from an Image and Metadata
画像とカメラメタデータを入力とするDNNで、主要なノイズ源をリアルタイムかつ省メモリで推定する手法を提案し、合成・実世界データセットで有効性を示した。
著者: Maik Wischow, Patrick Irmisch, Anko Boerner, Guillermo Gallego
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
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.
