• Amortised MAP Inference for Image Super-resolution
• The Unreasonable Effectiveness of Texture Transfer for Single Image Super-resolution
• Deep Learning for Single Image Super-Resolution: A Brief Review
• Deep Network Interpolation for Continuous Imagery Effect Transition
• TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
• Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
• Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
• Image Super-Resolution by Neural Texture Transfer
• Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
• A Matrix-in-matrix Neural Network for Image Super Resolution
• Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
• Fast Spatio-Temporal Residual Network for Video Super-Resolution
• Blind Super-ResolutionWith Iterative Kernel Correction
• A Deep Journey into Super-resolution: A Survey
• Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
• Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
• Multi-scale deep neural networks for real image super-resolution
• Spatio-Temporal Filter Adaptive Network for Video Deblurring
• Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
• Trinity of Pixel Enhancement: a Joint Solution for Demosaicking, Denoising and Super-Resolution
• EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
• Towards Real Scene Super-Resolution with Raw Images
• Suppressing Model Overfitting for Image Super-Resolution Networks
• Densely Residual Laplacian Super-Resolution
• FC²N: Fully Channel-Concatenated Network for Single Image Super-Resolution
• Gated Multiple Feedback Network for Image Super-Resolution
• Single Image Super-Resolution via CNN Architectures and TV-TV Minimization
• Hybrid Residual Attention Network for Single Image Super Resolution
• Progressive Perception-Oriented Network for Single Image Super-Resolution
• RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
• SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
• Edge-Informed Single Image Super-Resolution
• Deformable Non-local Network for Video Super-Resolution
• s-LWSR: Super Lightweight Super-Resolution Network
• Efficient Residual Dense Block Search for Image Super-Resolution
• Lightweight Image Super-Resolution with Information Multi-distillation Network
• Multi-grained Attention Networks for Single Image Super-Resolution
• Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution
• FISR: Deep Joint Frame Interpolation and Super-Resolution with A Multi-scale Temporal Loss
• Adaptive Densely Connected Single Image Super-Resolution
• Scale-wise Convolution for Image Restoration
• Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization
• Non-Local Recurrent Network for Image Restoration
• Camera Lens Super-Resolution
• Encoder-Decoder Residual Network for Real Super-resolution
• Second-order Attention Network for Single Image Super-Resolution
• Pixel Recursive Super Resolution
• Balanced Two-Stage Residual Networks for Image Super-Resolution
• Fast Image Restoration with Multi-bin Trainable Linear Units
• Image Super-Resolution via Dual-State Recurrent Networks
• ODE-inspired Network Design for Single Image Super-Resolution
• Deep Video Super-Resolution Network Using Dynamic Upsampling FiltersWithout Explicit Motion Compensation
• Multi-scale Residual Network for Image Super-Resolution
• Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
• xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
• Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection
• Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution
• Residual Non-local Attention Networks for Image Restoration
• Naive Bayes Super-Resolution Forest
• Fast and Accurate Image Upscaling with Super-Resolution Forests
• SRFeat: Single Image Super-Resolution with Feature DiscriminationDeep neural networks for direct, featureless learning through observation: the case of 2d spin models
• Single Image Super-resolution from Transformed Self-Exemplars
• Image Super-Resolution via Deep Recursive Residual Network
• A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
• Seven ways to improve example-based single image super resolution
• Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
• Image Super-Resolution Using Dense Skip Connections
• Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network
• Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks
• Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks
• CFSNet: Toward a Controllable Feature Space for Image Restoration
• Fast and Accurate Image Super-Resolution Using A Combined Loss
• Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations
• Learning Deep CNN Denoiser Prior for Image Restoration
• Residual Networks for Light Field Image Super-Resolution
• Kernel Modeling Super-Resolution on Real Low-Resolution Images