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Mechanical image stabilization using actuated gimbals enables capturinglong-exposure shots without suffering from blur due to camera motion. Thesedevices, however, are often physically cumbersome and expensive, limiting theirwidespread use. In this work, we propose to digitally emulate a mechanicallystabilized system from the input of a fast unstabilized camera. To exploit thetrade-off between motion blur at long exposures and low SNR at short exposures,we train a CNN that estimates a sharp high-SNR image by aggregating a burst ofnoisy short-exposure frames, related by unknown motion. We further suggestlearning the burst's exposure times in an end-to-end manner, thus balancing thenoise and blur across the frames. We demonstrate this method's advantage overthe traditional approach of deblurring a single image or denoising afixed-exposure burst on both synthetic and real data.

Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times