The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Open accessAbstract
Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.
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Cites
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On Low-Resolution Face Recognition in the Wild: Comparisons and New Techniques
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End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
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Multi-scale dynamic feature encoding network for image demoiréing
Cheng, X., Fu, Z., & Yang, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00432 · Cited by 50
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CAAT: Image super-resolution algorithm via channel attention and transformer
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Metadata
- Authors
- Lai, W.-S., Huang, J.-B., Ahuja, N., & Yang, M.-H.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5835-5843
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2607041014
- Cited by
- 2,976
- References
- 9
- FWCI
- 72.16
- Type
- conference paper
- Language
- en
- Open access
- green
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