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When taking photos in dim-light environments, due to the small amount of light entering, the shot images are usually extremely dark, with a great deal of noise, and the color cannot reflect real-world color.
High dynamic range display systems
H. Seetzen, W. Heidrich, W. Stuerzlinger, G. Ward, L. Whitehead, M. Trentacoste, A. Ghosh, and A. Vorozcovs · 2004
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A non-local algorithm for image denoising
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Cmos image sensors
A. El Gamal and H. Eltoukhy · 2005
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Video denoising by sparse 3d transform-domain collaborative filtering
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Mobile imaging: the big challenge of the small pixel
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Estimating aggregate consumer preferences from online product reviews
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T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, and S. Khudanpur · 2010
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H. Chen, Y. Zhang, M. K. Kalra, F. Lin, Y. Chen, P. Liao, J. Zhou, and G. Wang · 2017
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Benchmarking denoising algorithms with real photographs
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Deep convolutional denoising of low-light images
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang · 2017
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Learning to see in the dark
C. Chen, Q. Chen, J. Xu, and V. Koltun · 2018
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Deep burst denoising
C. Godard, K. Matzen, and M. Uyttendaele · 2018
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A survey of edge-preserving image denoising methods
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Deepisp: Toward learning an end-to-end image processing pipeline
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