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 3251–3300 of 3874 papers

TitleStatusHype
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution—0
Image restoration quality assessment based on regional differential information entropy—0
ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems—0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Regularization via deep generative models: an analysis point of view—0
Regularized estimation of image statistics by Score Matching—0
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach—0
Regularizing Differentiable Architecture Search with Smooth Activation—0
Relative Pixel Prediction For Autoregressive Image Generation—0
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution—0
RELD: Regularization by Latent Diffusion Models for Image Restoration—0
Reliability-based Mesh-to-Grid Image Reconstruction—0
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art—0
GlyphDiffusion: Text Generation as Image Generation—0
RepNet-VSR: Reparameterizable Architecture for High-Fidelity Video Super-Resolution—0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Representing Flow Fields with Divergence-Free Kernels for Reconstruction—0
Resampling and super-resolution of hexagonally sampled images using deep learning—0
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Feature Aggregation Network for Image Super-Resolution—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
Residual Networks for Light Field Image Super-Resolution—0
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Resolution enhancement in scanning electron microscopy using deep learning—0
Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence—0
Resolution Invariant Autoencoder—0
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
Rethinking Image Evaluation in Super-Resolution—0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Rethinking Implicit Neural Representations for Vision Learners—0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution—0
Revealing economic facts: LLMs know more than they say—0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
Neural Nearest Neighbors NetworksCode0
Extreme-scale Talking-Face Video Upsampling with Audio-Visual PriorsCode0
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution MicroscopyCode0
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Neural Architecture Search for Deep Image PriorCode0
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified