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 671–680 of 3874 papers

TitleStatusHype
DeblurSR: Event-Based Motion Deblurring Under the Spiking RepresentationCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIsCode1
Dual-Camera Super-Resolution with Aligned Attention ModulesCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Human Guided Ground-truth Generation for Realistic Image Super-resolutionCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified