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 311–320 of 3874 papers

TitleStatusHype
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super ResolutionCode0
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
Deep learning for temporal super-resolution 4D Flow MRICode0
State-of-the-Art Transformer Models for Image Super-Resolution: Techniques, Challenges, and Applications—0
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation—0
C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-ResolutionCode0
Diff-Ensembler: Learning to Ensemble 2D Diffusion Models for Volume-to-Volume Medical Image Translation—0
SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis—0
Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified