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

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Showing 601–625 of 3874 papers

TitleStatusHype
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-ResolutionCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Boosting Video Super Resolution with Patch-Based Temporal Redundancy OptimizationCode1
Rethinking Alignment in Video Super-Resolution TransformersCode1
Enhancing Space-time Video Super-resolution via Spatial-temporal Feature InteractionCode1
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical FidelityCode1
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural NetworksCode1
Cross-receptive Focused Inference Network for Lightweight Image Super-ResolutionCode1
Learning Local Implicit Fourier Representation for Image WarpingCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Variational Deep Image RestorationCode1
Structured Sparsity Learning for Efficient Video Super-ResolutionCode1
Hypernetwork-Based Adaptive Image RestorationCode1
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT ScansCode1
Real-World Light Field Image Super-Resolution via Degradation ModulationCode1
Real-World Image Super-Resolution by Exclusionary Dual-LearningCode1
Recurrent Video Restoration Transformer with Guided Deformable AttentionCode1
ShuffleMixer: An Efficient ConvNet for Image Super-ResolutionCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Image Super-resolution with An Enhanced Group Convolutional Neural NetworkCode1
SelfReformer: Self-Refined Network with Transformer for Salient Object DetectionCode1
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video RestorationCode1
Residual Local Feature Network for Efficient Super-ResolutionCode1
Blueprint Separable Residual Network for Efficient Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified