SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2826–2850 of 3874 papers

TitleStatusHype
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model InferenceCode1
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—0
Single Image Super-Resolution via a Holistic Attention NetworkCode1
Revisiting Temporal Modeling for Video Super-resolutionCode1
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling NetworkCode1
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANCode1
Component Divide-and-Conquer for Real-World Image Super-ResolutionCode1
Sub-Pixel Back-Projection Network For Lightweight Single Image Super-ResolutionCode1
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
Deep Photo Cropper and Enhancer—0
Video Super-Resolution with Recurrent Structure-Detail NetworkCode1
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution UnitCode1
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Binarized Neural Network for Single Image Super Resolution—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified