The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Open accessAbstract
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Since the standard mean squared loss is not well suited for measuring perceptual image quality, we introduce a composite perceptual error function that combines content, color and texture losses. The first two losses are defined analytically, while the texture loss is learned in an adversarial fashion. We also present DPED, a large-scale dataset that consists of real photos captured from three different phones and one high-end reflex camera. Our quantitative and qualitative assessments reveal that the enhanced image quality is comparable to that of DSLR-taken photos, while the methodology is generalized to any type of digital camera.
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Image-to-Image Translation with Conditional Adversarial Networks
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
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Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00475 · Cited by 760
Self-citationAI Benchmark: Running Deep Neural Networks on Android Smartphones
Ignatov, A., Timofte, R., Chou, W., Wang, K., Wu, M., Hartley, T., & Van Gool, L. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11021-5_19 · Cited by 347
Self-citationSimultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Jahidul Islam, M., Luo, P., & Sattar, J. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.018 · Cited by 269
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment
Wang, R., Xu, X., Fu, C.-W., Lu, J., Yu, B., & Jia, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00956 · Cited by 145
Metadata
- Authors
- Ignatov, A., Kobyshev, N., Timofte, R., & Vanhoey, K.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3297-3305
- OpenAlex
- W2607202125
- Cited by
- 582
- References
- 6
- FWCI
- 11.41
- Type
- conference paper
- Language
- en
- Open access
- green
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