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 3251–3275 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified