The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Loss Functions for Image Restoration With Neural Networks
Abstract
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is ℓ2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · cited 2×
“We use two state-of-the-art metrics for image quality: the structural similarity index (SSIM [13]) and the multiscale structural similarity index (MS-SSIM [14]).”
BackgroundMethodMean squared error: Love it or leave it? A new look at Signal Fidelity Measures
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Cited by
Digging Into Self-Supervised Monocular Depth Estimation
Godard, C., Aodha, O. M., Firman, M., & Brostow, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00393 · Cited by 2,561
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Mahjourian, R., Wicke, M., & Angelova, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00594 · Cited by 775
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Žbontar, J., Knöll, F., Sriram, A., Murrell, T., Huang, Z., Muckley, M. J., Defazio, A., Stern, R., Johnson, P., Bruno, M. T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08839 · Cited by 528
You, C., Cong, W., Vannier, M. W., Saha, P. K., Hoffman, E. A., Wang, G., Li, G., Zhang, Y., Zhang, X., Shan, H., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2922960 · Cited by 459
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
Kim, B., Azevedo, V. C., Thuerey, N., Kim, T., Gross, M., & Solenthaler, B. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13619 · Cited by 372
Metadata
- Authors
- Zhao, H., Gallo, O., Frosio, I., & Kautz, J.
- Venue
- IEEE Transactions on Computational Imaging
- Published
- 2017-03
- Volume
- 3
- Issue
- 1
- Pages
- 47-57
- ISSN
- 2333-9403 (ISSN-L)
- 2334-0118
- OpenAlex
- W2562637781
- Cited by
- 2,641
- References
- 7
- FWCI
- 76.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2017). Loss Functions for Image Restoration With Neural Networks. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/tci.2016.2644865
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