A not-really-curated list of image and video processing using deep learning, inspired by awesome-php, awesome-computer-vision and awesome-deep-vision
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Scale-recurrent Network for Deep Image Deblurring (2018), X. Tao et al., ArXiv [pdf]
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Fast and Accurate Reconstruction of Compressed Color Light Field (2018), O. Nabati et al., ArXiv [pdf]
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DeepISP: Learning End-to-End Image Processing Pipeline (2018), E. Schwartz et al., ArXiv [pdf]
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Reblur2Deblur: Deblurring Videos via Self-Supervised Learning (2018), H. Chen et al., ArXiv [pdf]
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Frame-Recurrent Video Super-Resolution (2018), M. Sajjadi et al., ArXiv [pdf]
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Deep Burst Denoising (2017), C. Gordard et al. [pdf]
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Motion Blur Kernel Estimation via Deep Learning (2017), X. Xu, et al. TIP [web]
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Deep Video Deblurring for Hand-held cameras (2017), S. Su et al., CVPR [pdf]
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Burst Denoising with Kernel Prediction Networks (2017), B. Mildenhall et al., [pdf]
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Universal Denoising Networks: A Novel CNN-based Network Architecture for Image Denoising (2017), S. Lefkimmiatis, ArXiv [pdf]
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InverseNet: Solving Inverse Problems with Splitting Networks (2017), K. Fan et al., [pdf]
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Block-matching convolutional neural network for image denoising (2017), B. Ahn, NI. Cho, [pdf]
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EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2017), M. Sajjadi et al., ArXiv [pdf] [code]
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Learning Blind Motion Deblurring (2017), P. Wieschollek et al. ArXiv [pdf]
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Online Video Deblurring via Dynamic Temporal Blending Network (2017), T. H. Kim et al., ArXiv [pdf]
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Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks (2017), J. van Amersfoort et al., [pdf]
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Deep class aware denoising (2017), T. Remez et al., ArXiv [pdf]
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Image Restoration using Autoencoding Priors (2017), Bigdeli and Zwicker, ArXiv [[pdf]
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On-Demand Learning for Deep Image Restoration (2017), R. Gao and K. Grauman, ICCV [pdf] [code]
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DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks (2017), O. Kupyn et al., ArXiv [pdf] [code]
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One Network to Solve Them All — Solving Linear Inverse Problems using Deep Projection Models,(2017), J.H. Rick Chang et al., ICCV [pdf]
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Solving ill-posed inverse problems using iterative deep neural network (2017), O. Ozan and J. Adler, Inverse Problems [pdf]
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Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data (2017), S. Diamond [pdf]
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Deep Mean-Shift Priors for Image Restoration (2017), S. Bigdeli et al. [pdf]
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Learning Deep CNN Denoiser Prior for Image Restoration (2017), K. Zhang et al., CVPR [pdf] [code]
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Image Restoration: From Sparse and Low-Rank Priors to Deep Priors (2017), L. Zhang and W. Zuo
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An inner-loop free solution to inverse problems using deep neural networks (2017), K. Fai et al., NIPS [pdf]
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Deep convolutional framelets: A general deep learning for inverse problems (2017), J.C. Ye and Y.S. Han [pdf]
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Photo-realistic single image super-resolution using a generative adversarial network (2017), C. Ledig et al., [pdf]
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Image Super-Resolution via Deep Recursive Residual Network (2017), Y. Tai et al., CVPR [pdf] [code]
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Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (2017), W.S. Lai et al., CVPR [pdf]
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Motion Deblurring in the Wild (2017), M. Noroozi et al., [pdf]
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Discriminative Transfer Learning for General Image Restoration (2017), Xiao et al., ArXiv [pdf]
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Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (2017), Meinhardt et al., ICCV, [pdf]
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Kernel-predicting convolutional networks for denoising Monte Carlo renderings (2017), S. Bako et al., SIGGRAPH [pdf]
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Video Frame Interpolation via Adaptive Convolution (2017), S. Niklaus et al., ICCV [pdf]
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Video Frame Synthesis using Deep Voxel Flow (2017) Z. Liu et al., [pdf]
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Interactive reconstruction of monte carlo image sequences using a recurrent denoising autoencoder (2017), C. Chaitanya, et al., TOG [pdf]
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Deep Joint Demosaicking and Denoising (2016), M. Gharbi et al., Siggraph Asia [pdf]
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Deep Convolutional Neural Network for Inverse Problems in Imaging (2016), Kyong Hwan Jin et al., ArXiv [pdf]
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Non-local color image denoising with convolutional neural networks (2016), S. Lefkimmiatis, CVPR [pdf]
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A Neural Approach to Blind Motion Deblurring (2016), A. Chakrabarti [pdf]
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks (2016), J. Kim et al., CVPR [pdf]
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Image super-resolution using deep convolutional networks (2016), C. Dong et al., PAMI [pdf]
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Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring (2016), S. Nah [pdf]
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Deep RNNs for video denoising (2016), X. Chen et al., SPIE
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Imageto-image translation with conditional adversarial networks (2016), P. Isola et al., [pdf]
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network (2016), W. Shi et al., CVPR [pdf]
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Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections (2016), X. Mao et al., NIPS [pdf]
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2016), K. Zhang et al., [pdf] [code (Matlab)] [code (Python)]
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Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal (2015), J. Sun et al., CVPR [pdf]
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Deep networks for image super-resolution with sparse prior (2015), Z. Wang et al., CVPR [pdf]
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Super-resolution with deep convolutional sufficient statistics (2015), J. Bruna et al. [pdf]
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Learning a Deep Convolutional Network for Image Super-Resolution (2014) C. Dong et al., ECCV [pdf]
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Deep network cascade for image super-resolution (2014), Z. Cui et al., ECCV [pdf]
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Deep convolutional neural network for image deconvolution (2014), L. Xu et al., NIPS [pdf]
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Learning to deblur (2014), C. Schuler et al., PAMI [pdf]