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 126–150 of 3874 papers

TitleStatusHype
Misalignment-Robust Frequency Distribution Loss for Image TransformationCode2
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion ModelsCode2
See More Details: Efficient Image Super-Resolution by Experts MiningCode2
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token DictionaryCode2
Video Super-Resolution Transformer with Masked Inter&Intra-Frame AttentionCode2
Transforming Image Super-Resolution: A ConvFormer-based Efficient ApproachCode2
CFAT: Unleashing Triangular Windows for Image Super-resolutionCode2
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-ResolutionCode2
HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion ModelsCode2
Kandinsky 3.0 Technical ReportCode2
Zooming Out on Zooming In: Advancing Super-Resolution for Remote SensingCode2
Neural Fields with Thermal Activations for Arbitrary-Scale Super-ResolutionCode2
CoSeR: Bridging Image and Language for Cognitive Super-ResolutionCode2
Swift Parameter-free Attention Network for Efficient Super-ResolutionCode2
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Dual Aggregation Transformer for Image Super-ResolutionCode2
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth EstimationCode2
Efficient Mixed Transformer for Single Image Super-ResolutionCode2
Denoising Diffusion Models for Plug-and-Play Image RestorationCode2
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
Enhancing Video Super-Resolution via Implicit Resampling-based AlignmentCode2
Omni Aggregation Networks for Lightweight Image Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified