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Natural videos captured by consumer cameras often suffer from low framerateand motion blur due to the combination of dynamic scene complexity, lens andsensor imperfection, and less than ideal exposure setting. As a result,computational methods that jointly perform video frame interpolation anddeblurring begin to emerge with the unrealistic assumption that the exposuretime is known and fixed. In this work, we aim ambitiously for a more realisticand challenging task - joint video multi-frame interpolation and deblurringunder unknown exposure time. Toward this goal, we first adopt a variant ofsupervised contrastive learning to construct an exposure-aware representationfrom input blurred frames. We then train two U-Nets for intra-motion andinter-motion analysis, respectively, adapting to the learned exposurerepresentation via gain tuning. We finally build our video reconstructionnetwork upon the exposure and motion representation by progressiveexposure-adaptive convolution and motion refinement. Extensive experiments onboth simulated and real-world datasets show that our optimized method achievesnotable performance gains over the state-of-the-art on the joint video x8interpolation and deblurring task. Moreover, on the seemingly implausible x16interpolation task, our method outperforms existing methods by more than 1.5 dBin terms of PSNR.

Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time