ML Anthology

Burst Denoising with Kernel Prediction Networks

Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, Robert Carroll
CVPR 2018
doi:10.1109/CVPR.2018.00265 /cvpr/2018/mildenhall2018cvpr-burst/

Abstract

We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.

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Cite

Text

Mildenhall et al. "Burst Denoising with Kernel Prediction Networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00265

Markdown

[Mildenhall et al. "Burst Denoising with Kernel Prediction Networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/mildenhall2018cvpr-burst/) doi:10.1109/CVPR.2018.00265

BibTeX

@inproceedings{mildenhall2018cvpr-burst,
  title     = {{Burst Denoising with Kernel Prediction Networks}},
  author    = {Mildenhall, Ben and Barron, Jonathan T. and Chen, Jiawen and Sharlet, Dillon and Ng, Ren and Carroll, Robert},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00265},
  url       = {https://mlanthology.org/cvpr/2018/mildenhall2018cvpr-burst/}
}