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An event camera detects per-pixel intensity difference and producesasynchronous event stream with low latency, high dynamic range, and low powerconsumption. As a trade-off, the event camera has low spatial resolution. Wepropose an end-to-end network to reconstruct high resolution, high dynamicrange (HDR) images directly from the event stream. We evaluate our algorithm onboth simulated and real-world sequences and verify that it captures finedetails of a scene and outperforms the combination of the state-of-the-artevent to image algorithms with the state-of-the-art super resolution schemes inmany quantitative measures by large margins. We further extend our method byusing the active sensor pixel (APS) frames or reconstructing imagesiteratively.

Learning to Super Resolve Intensity Images from Events