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 801–810 of 3874 papers

TitleStatusHype
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsCode1
Temporal Modulation Network for Controllable Space-Time Video Super-ResolutionCode1
SRWarp: Generalized Image Super-Resolution under Arbitrary TransformationCode1
A Two-Stage Attentive Network for Single Image Super-ResolutionCode1
Attention in Attention Network for Image Super-ResolutionCode1
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Image Super-Resolution via Iterative RefinementCode1
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-ResolutionCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified