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In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR).
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U-net: Convolutional networks for biomedical image segmentation
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Going deeper with convolutions
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Deep video deblurring for hand-held cameras
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C. Chen, Q. Chen, J. Xu, and V. Koltun · 2018
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S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang · 2018
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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A. Odena, V. Dumoulin, and C. Olah · 2016
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Context encoders: Feature learning by inpainting
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
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Automatic photo adjustment using deep neural networks
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Improving color reproduction accuracy on cameras
H. C. Karaimer and M. S. Brown · 2018
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F. Kokkinos and S. Lefkimmiatis · 2018
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Universal denoising networks: A novel CNN architecture for image denoising
S. Lefkimmiatis · 2018
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Image inpainting for irregular holes using partial convolutions
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PieAPP: Perceptual image-error assessment through pairwise preference
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DeepISP: Towards learning an end-to-end image processing pipeline
E. Schwartz, R. Giryes, and A. M. Bronstein · 2018
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Learned perceptual image enhancement
H. Talebi and P. Milanfar · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
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