Digital Gimbal:

Abstract

Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized system from the input of a fast unstabilized camera. To exploit the trade-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 of noisy short-exposure frames, related by unknown motion. We further suggest learning the burst’s exposure times in an end-to-end manner, thus balancing the noise and blur across the frames. We demonstrate this method’s advantage over the traditional approach of deblurring a single image or denoising a fixed-exposure burst on both synthetic and real data.

Links

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Cite

@InProceedings{Dahary_2021_CVPR,

author = {Dahary, Omer and Jacoby, Matan and Bronstein, Alex M.},

title = {Digital Gimbal: End-to-End Deep Image Stabilization With Learnable Exposure Times},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

month = {June},

year = {2021},

pages = {11936-11945}

}

References

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2. Ben Mildenhall, Jonathan T Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, and Robert Carroll. Burst denoising with kernel prediction networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2502–2510, 2018.