  Event cameras are ideally suited to capture High Dynamic Range (HDR) visual
information without blur but provide poor imaging capability for static or
slowly varying scenes. Conversely, conventional image sensors measure absolute
intensity of slowly changing scenes effectively but do poorly on HDR or quickly
changing scenes. In this paper, we present an asynchronous linear filter
architecture, fusing event and frame camera data, for HDR video reconstruction
and spatial convolution that exploits the advantages of both sensor modalities.
The key idea is the introduction of a state that directly encodes the
integrated or convolved image information and that is updated asynchronously as
each event or each frame arrives from the camera. The state can be read-off
as-often-as and whenever required to feed into subsequent vision modules for
real-time robotic systems. Our experimental results are evaluated on both
publicly available datasets with challenging lighting conditions and fast
motions, along with a new dataset with HDR reference that we provide. The
proposed AKF pipeline outperforms other state-of-the-art methods in both
absolute intensity error (69.4% reduction) and image similarity indexes
(average 35.5% improvement). We also demonstrate the integration of image
convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an
application of our architecture.
