The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Deep joint demosaicking and denoising
Open accessAbstract
Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moiré or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.
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Cites
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Cited by
Chen, C., Chen, Q., Xu, J., & Koltun, V. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00347 · Cited by 1,376
Unprocessing Images for Learned Raw Denoising
Brooks, T., Mildenhall, B., Xue, T., Chen, J., Sharlet, D., & Barron, J. T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01129 · Cited by 473
Burst Denoising with Kernel Prediction Networks
Mildenhall, B., Barron, J. T., Chen, J., Sharlet, D., Ng, R., & Carroll, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00265 · Cited by 404
Fast Image Processing with Fully-Convolutional Networks
Chen, Q., Xu, J., & Koltun, V. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.273 · Cited by 341
Kernel-predicting convolutional networks for denoising Monte Carlo renderings
Bako, S., Vogels, T., Mcwilliams, B., Meyer, M., NováK, J., Harvill, A., Sen, P., Derose, T., & Rousselle, F. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073708 · Cited by 308
Metadata
- Authors
- Gharbi, M., Chaurasia, G., Paris, S., & Durand, F.
- Venue
- ACM Transactions on Graphics
- Published
- 2016-11-11
- Volume
- 35
- Issue
- 6
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2556872594
- Cited by
- 473
- References
- 56
- FWCI
- 27.15
- Type
- journal article
- Language
- en
- Open access
- green
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