IMU-Blur

IMU-Blur commenced our evaluation by randomly selecting 8350 clear images (aka. backgrounds) from existing image datasets~\cite{zhou2017places,quattoni2009recognizing}. By capturing IMU data during the motion blur induced by the RealSense D455i camera, we synthesized a dataset of 8350 blurred images accompanied by corresponding blur heat maps. Ultimately, this dataset, namely IMU-Blur, contains 6680 triplets for training and 1670 triplets for testing. Our IMU-Blur dataset has significant advantages over widely used datasets such as GoPro~\cite{nah2017deep} and RealBlur~\cite{rim2020real}, which contain limited scene variations. It includes images captured in 6680 environments, eliminating the interference of features present in repeated scenes for network learning. Unlike existing blurred datasets mainly recorded by high-speed cameras, IMU-Blur is more accessible to synthesize towards massive-produced and real-world motion blur.