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 2576–2600 of 3874 papers

TitleStatusHype
PAG-Net: Progressive Attention Guided Depth Super-resolution Network—0
Capsule GAN for Prostate MRI Super-Resolution—0
Can SAM Boost Video Super-Resolution?—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?—0
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening—0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Uncertainty Quantification via Neural Posterior Principal Components—0
Panoramas from Photons—0
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?—0
PAON: A New Neuron Model using Padé Approximants—0
ParaDiS: Parallelly Distributable Slimmable Neural Networks—0
Calcium oscillation on homogeneous and heterogeneous networks of ryanodine receptor—0
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability—0
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network—0
Parallel Statistical Multi-resolution Estimation—0
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming—0
Particle-Filtering-based Latent Diffusion for Inverse Problems—0
C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales—0
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images—0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified