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 1151–1175 of 3874 papers

TitleStatusHype
FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion—0
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image SegmentationCode1
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling—0
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation—0
Stochastic interpolants with data-dependent couplingsCode1
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics—0
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution—0
A Restoration Network as an Implicit Prior—0
RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency WavefrontsCode1
Forecasting Tropical Cyclones with Cascaded Diffusion ModelsCode1
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
A New Real-World Video Dataset for the Comparison of Defogging Algorithms—0
Prompt-tuning latent diffusion models for inverse problems—0
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super ResolutionCode0
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes—0
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution—0
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image SynthesisCode0
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis—0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified