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 1541–1550 of 3874 papers

TitleStatusHype
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral ImagesCode0
A Unified Plug-and-Play Algorithm with Projected Landweber Operator for Split Convex Feasibility Problems—0
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model—0
Joint Reconstruction and Spatial Super-Resolution of Hyper-Spectral CTIS Images via Multi-Scale RefinementCode0
MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling—0
Harnessing Multi-resolution and Multi-scale Attention for Underwater Image RestorationCode0
Webcam-based Pupil Diameter Prediction Benefits from UpscalingCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Angle of Arrival Estimation with Transformer: A Sparse and Gridless Method with Zero-Shot Capability—0
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified