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 276–300 of 3874 papers

TitleStatusHype
Attention Beats Linear for Fast Implicit Neural Representation GenerationCode1
A New Dataset and Framework for Real-World Blurred Images Super-ResolutionCode1
Large Kernel Distillation Network for Efficient Single Image Super-ResolutionCode1
MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby ReferencesCode1
Region Attention Transformer for Medical Image RestorationCode1
Pairwise Distance Distillation for Unsupervised Real-World Image Super-ResolutionCode1
Real HSI-MSI-PAN image dataset for the hyperspectral/multi-spectral/panchromatic image fusion and super-resolution fieldsCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Preserving Full Degradation Details for Blind Image Super-ResolutionCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-ResolutionCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-ResolutionCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential EquationsCode1
SuperFormer: Volumetric Transformer Architectures for MRI Super-ResolutionCode1
ReLUs Are Sufficient for Learning Implicit Neural RepresentationsCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-ResolutionCode1
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion TeacherCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Exploring the Low-Pass Filtering Behavior in Image Super-ResolutionCode1
Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion PriorCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified