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 2871–2880 of 3874 papers

TitleStatusHype
Dual-Stream Fusion Network for Spatiotemporal Video Super-ResolutionCode0
Transformers in Vision: A Survey—0
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
Event Stream Super-Resolution via Spatiotemporal Constraint Learning—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Context Reasoning Attention Network for Image Super-Resolution—0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective—0
Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified