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Neural Radiance Field (NeRF) has gained considerable attention recently for 3D scene reconstruction and novel view synthesis due to its remarkable synthesis quality.
Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
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Total variation blind deconvolution
Tony F Chan and Chiu-Kwong Wong · 1998
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Richardson-lucy deblurring for scenes under a projective motion path
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Xiaogang Chen, Feng Li, Jie Yang, and Jingyi Yu · 2012
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Non-uniform motion deblurring for bilayer scenes
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Unnatural l0 sparse representation for natural image deblurring
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Adam: A method for stochastic optimization
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Generalized video deblurring for dynamic scenes
Tae Hyun Kim and Kyoung Mu Lee · 2015
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Structure-from-motion revisited
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Pixelwise view selection for unstructured multi-view stereo
Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm · 2016
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Blind image deconvolution: theory and applications
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Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee · 2017
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Motion deblurring in the wild
Mehdi Noroozi, Paramanand Chandramouli, and Paolo Favaro · 2017
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Joint estimation of camera pose, depth, deblurring, and super-resolution from a blurred image sequence
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Deep video deblurring for hand-held cameras
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Blender - a 3D modelling and rendering package
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Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiří Matas · 2018
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Scale-recurrent network for deep image deblurring
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Self-calibrating neural radiance fields
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World from blur
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Nerf: Representing scenes as neural radiance fields for view synthesis
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Cascaded deep video deblurring using temporal sharpness prior
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T Barron, and Ren Ng · 2020
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