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 2031–2040 of 3874 papers

TitleStatusHype
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling—0
Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks—0
Directional diffusion models for graph representation learning—0
Super-Resolution of BVOC Emission Maps Via Domain AdaptationCode0
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images—0
HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models—0
Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology—0
Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models—0
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—0
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified