The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Fast Image Processing with Fully-Convolutional Networks
Open accessAbstract
We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator's action. After training, the original operator need not be run at all. The trained network operates at full resolution and runs in constant time. We investigate the effect of network architecture on approximation accuracy, runtime, and memory footprint, and identify a specific architecture that balances these considerations. We evaluate the presented approach on ten advanced image processing operators, including multiple variational models, multiscale tone and detail manipulation, photographic style transfer, nonlocal dehazing, and nonphoto-realistic stylization. All operators are approximated by the same model. Experiments demonstrate that the presented approach is significantly more accurate than prior approximation schemes. It increases approximation accuracy as measured by PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from 27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to the most accurate prior approximation scheme, while being the fastest. We show that our models generalize across datasets and across resolutions, and investigate a number of extensions of the presented approach.
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Learning to Optimize: Training Deep Neural Networks for Interference Management
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Gibson Env: Real-World Perception for Embodied Agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763
Classification-Driven Dynamic Image Enhancement
Sharma, V., Diba, A., Neven, D., Brown, M. S., Gool, L. V., & Stiefelhagen, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00424 · Cited by 66
Learning to denoise astronomical images with U-nets
Vojtekova, A., Lieu, M., Valtchanov, I., Altieri, B., Old, L., Chen, Q., & Hroch, F. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3567 · Cited by 63
Self-citation
Metadata
- Authors
- Chen, Q., Xu, J., & Koltun, V.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 2516-2525
- OpenAlex
- W2963452532
- Cited by
- 341
- References
- 80
- FWCI
- 10.76
- Type
- conference paper
- Language
- en
- Open access
- green
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