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Event cameras differ from conventional RGB cameras in that they produceasynchronous data sequences. While RGB cameras capture every frame at a fixedrate, event cameras only capture changes in the scene, resulting in sparse andasynchronous data output. Despite the fact that event data carries usefulinformation that can be utilized in motion deblurring of RGB cameras,integrating event and image information remains a challenge. Recentstate-of-the-art CNN-based deblurring solutions produce multiple 2-D eventframes based on the accumulation of event data over a time period. In most ofthese techniques, however, the number of event frames is fixed and predefined,which reduces temporal resolution drastically, particularly for scenarios whenfast-moving objects are present or when longer exposure times are required. Itis also important to note that recent modern cameras (e.g., cameras in mobilephones) dynamically set the exposure time of the image, which presents anadditional problem for networks developed for a fixed number of event frames. ALong Short-Term Memory (LSTM)-based event feature extraction module has beendeveloped for addressing these challenges, which enables us to use adynamically varying number of event frames. Using these modules, we constructeda state-of-the-art deblurring network, Deformable Convolutions and LSTM-basedFlexible Event Frame Fusion Network (DLEFNet). It is particularly useful forscenarios in which exposure times vary depending on factors such as lightingconditions or the presence of fast-moving objects in the scene. It has beendemonstrated through evaluation results that the proposed method can outperformthe existing state-of-the-art networks for deblurring task in synthetic andreal-world data sets.

Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring