End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution-Reference-Cited by-同舟云学术

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End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution

Published:2021-06 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Xing Wenzhu,Egiazarian Karen

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9577055/9577056/09578586.pdf?arnumber=9578586

Cited by 51 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. RSTC: Residual Swin Transformer Cascade to approximate Taylor expansion for image denoising;Computer Vision and Image Understanding;2024-11

2. An impartial framework to investigate demosaicking input embedding options;Computers & Graphics;2024-10

3. An Edge-Preserving Regularization Model for the Demosaicing of Noisy Color Images;Journal of Mathematical Imaging and Vision;2024-07-26

4. Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising;Network: Computation in Neural Systems;2024-07-11

5. Separating Spectral and Spatial Feature Aggregation for Demosaicking;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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