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 191–200 of 3874 papers

TitleStatusHype
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-ResolutionCode2
Arbitrary-Scale Video Super-Resolution with Structural and Textural PriorsCode2
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel FusionCode2
Effective Diffusion Transformer Architecture for Image Super-ResolutionCode2
PnP-Flow: Plug-and-Play Image Restoration with Flow MatchingCode2
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified