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 501–510 of 3874 papers

TitleStatusHype
An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural RepresentationCode1
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial LearningCode1
Consistent Direct Time-of-Flight Video Depth Super-ResolutionCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Efficient Image Super-Resolution using Vast-Receptive-Field AttentionCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
Anchor-based Plain Net for Mobile Image Super-ResolutionCode1
Conditional Variational Diffusion ModelsCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified