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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3001–3010 of 3874 papers

TitleStatusHype
Single Pair Cross-Modality Super Resolution—0
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks—0
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning—0
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization—0
What makes for good morphology representations for spatial omics?—0
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems—0
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified