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 1601–1610 of 3874 papers

TitleStatusHype
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning—0
M^3:Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask—0
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-DesignCode0
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning—0
Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration—0
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-ResolutionCode0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
Super-resolution imaging using super-oscillatory diffractive neural networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified