A not-really-curated list of image and video processing using deep learning, inspired by awesome-php, awesome-computer-vision and awesome-deep-vision

• Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning (2018), K. Yu et al., ArXiv [pdf]

• Adversarial Spatio-Temporal Learning for Video Deblurring (2018), K. Zhang et al., ArXiv [pdf]

• Deep Semantic Face Deblurring (2018), Z. Shen et al., ArXiv [pdf] [web]

• Learning to Maintain Natural Image Statistics (2018), R. Mechrez et al., ArXiv [pdf]

• Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks (2018), F. Kokkinos, S. Lefkimmiatis, [pdf]

• Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network (2018), N. Ahn et al., ArXiv [pdf]

• Fast and Accurate Single Image Super-Resolution via Information Distillation Network (2018), Z. Hui et al., ArXiv [pdf] [code]

• Scale-recurrent Network for Deep Image Deblurring (2018), X. Tao et al., ArXiv [pdf]

• Fast and Accurate Reconstruction of Compressed Color Light Field (2018), O. Nabati et al., ArXiv [pdf]

• DeepISP: Learning End-to-End Image Processing Pipeline (2018), E. Schwartz et al., ArXiv [pdf]

• Reblur2Deblur: Deblurring Videos via Self-Supervised Learning (2018), H. Chen et al., ArXiv [pdf]

• Frame-Recurrent Video Super-Resolution (2018), M. Sajjadi et al., ArXiv [pdf]

• Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (2017), K. Zhang, et al. [pdf]

• Deep Burst Denoising (2017), C. Gordard et al. [pdf]

• Motion Blur Kernel Estimation via Deep Learning (2017), X. Xu, et al. TIP [web]

• Deep Video Deblurring for Hand-held cameras (2017), S. Su et al., CVPR [pdf]

• Burst Denoising with Kernel Prediction Networks (2017), B. Mildenhall et al., [pdf]

• Universal Denoising Networks: A Novel CNN-based Network Architecture for Image Denoising (2017), S. Lefkimmiatis, ArXiv [pdf]

• InverseNet: Solving Inverse Problems with Splitting Networks (2017), K. Fan et al., [pdf]

• Block-matching convolutional neural network for image denoising (2017), B. Ahn, NI. Cho, [pdf]

• EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2017), M. Sajjadi et al., ArXiv [pdf] [code]

• Learning Blind Motion Deblurring (2017), P. Wieschollek et al. ArXiv [pdf]

• Online Video Deblurring via Dynamic Temporal Blending Network (2017), T. H. Kim et al., ArXiv [pdf]

• Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks (2017), J. van Amersfoort et al., [pdf]

• Deep class aware denoising (2017), T. Remez et al., ArXiv [pdf]

• Image Restoration using Autoencoding Priors (2017), Bigdeli and Zwicker, ArXiv [[pdf]

• On-Demand Learning for Deep Image Restoration (2017), R. Gao and K. Grauman, ICCV [pdf] [code]

• Deep Image Prior (2017), D. Ulyanov et al., [pdf] [code]

• DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks (2017), O. Kupyn et al., ArXiv [pdf] [code]

• One Network to Solve Them All — Solving Linear Inverse Problems using Deep Projection Models,(2017), J.H. Rick Chang et al., ICCV [pdf]

• Solving ill-posed inverse problems using iterative deep neural network (2017), O. Ozan and J. Adler, Inverse Problems [pdf]

• Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data (2017), S. Diamond [pdf]

• Deep Mean-Shift Priors for Image Restoration (2017), S. Bigdeli et al. [pdf]

• Learning Deep CNN Denoiser Prior for Image Restoration (2017), K. Zhang et al., CVPR [pdf] [code]

• Image Restoration: From Sparse and Low-Rank Priors to Deep Priors (2017), L. Zhang and W. Zuo

• An inner-loop free solution to inverse problems using deep neural networks (2017), K. Fai et al., NIPS [pdf]

• Deep convolutional framelets: A general deep learning for inverse problems (2017), J.C. Ye and Y.S. Han [pdf]

• Photo-realistic single image super-resolution using a generative adversarial network (2017), C. Ledig et al., [pdf]

• Image Super-Resolution via Deep Recursive Residual Network (2017), Y. Tai et al., CVPR [pdf] [code]

• Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (2017), W.S. Lai et al., CVPR [pdf]

• Motion Deblurring in the Wild (2017), M. Noroozi et al., [pdf]

• Discriminative Transfer Learning for General Image Restoration (2017), Xiao et al., ArXiv [pdf]

• Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (2017), Meinhardt et al., ICCV, [pdf]

• Kernel-predicting convolutional networks for denoising Monte Carlo renderings (2017), S. Bako et al., SIGGRAPH [pdf]

• Video Frame Interpolation via Adaptive Convolution (2017), S. Niklaus et al., ICCV [pdf]

• Video Frame Synthesis using Deep Voxel Flow (2017) Z. Liu et al., [pdf]

• Interactive reconstruction of monte carlo image sequences using a recurrent denoising autoencoder (2017), C. Chaitanya, et al., TOG [pdf]

• Deep Joint Demosaicking and Denoising (2016), M. Gharbi et al., Siggraph Asia [pdf]

• Deep Convolutional Neural Network for Inverse Problems in Imaging (2016), Kyong Hwan Jin et al., ArXiv [pdf]

• Non-local color image denoising with convolutional neural networks (2016), S. Lefkimmiatis, CVPR [pdf]

• A Neural Approach to Blind Motion Deblurring (2016), A. Chakrabarti [pdf]

• Accurate Image Super-Resolution Using Very Deep Convolutional Networks (2016), J. Kim et al., CVPR [pdf]

• Image super-resolution using deep convolutional networks (2016), C. Dong et al., PAMI [pdf]

• Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring (2016), S. Nah [pdf]

• Deep RNNs for video denoising (2016), X. Chen et al., SPIE

• Imageto-image translation with conditional adversarial networks (2016), P. Isola et al., [pdf]

• Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network (2016), W. Shi et al., CVPR [pdf]

• Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections (2016), X. Mao et al., NIPS [pdf]

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2016), K. Zhang et al., [pdf] [code (Matlab)] [code (Python)]

• Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal (2015), J. Sun et al., CVPR [pdf]

• Deep networks for image super-resolution with sparse prior (2015), Z. Wang et al., CVPR [pdf]

• Super-resolution with deep convolutional sufficient statistics (2015), J. Bruna et al. [pdf]

• Learning a Deep Convolutional Network for Image Super-Resolution (2014) C. Dong et al., ECCV [pdf]

• Deep network cascade for image super-resolution (2014), Z. Cui et al., ECCV [pdf]

• Deep convolutional neural network for image deconvolution (2014), L. Xu et al., NIPS [pdf]

• Learning to deblur (2014), C. Schuler et al., PAMI [pdf]

• Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising (2013), F. Agostinelli et al., NIPS [pdf]

• A machine learning approach for non-blind image deconvolution (2013), C. Schuler et al., CVPR [pdf]

• Image denoising and inpainting with deep neural networks (2012), J. Xie et al., NIPS [pdf]

• Image denoising: Can plain neural networks compete with BM3D? (2012), H.C. Burger et al., CVPR [pdf]