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

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Showing 1626–1650 of 3874 papers

TitleStatusHype
Bayesian Conditioned Diffusion Models for Inverse Problems—0
GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors—0
DDR: Exploiting Deep Degradation Response as Flexible Image DescriptorCode0
Towards Realistic Data Generation for Real-World Super-Resolution—0
Image Neural Field Diffusion Models—0
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance—0
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning—0
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method—0
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
Enhanced Semantic Segmentation Pipeline for WeatherProof Dataset ChallengeCode0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism—0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
SuperGaussian: Repurposing Video Models for 3D Super Resolution—0
Imitating the Functionality of Image-to-Image Models Using a Single Example—0
Hybrid attention structure preserving network for reconstruction of under-sampled OCT images—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
CoNO: Complex Neural Operator for Continous Dynamical Physical Systems—0
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions—0
Climate Variable Downscaling with Conditional Normalizing Flows—0
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?—0
Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search—0
Single image super-resolution based on trainable feature matching attention networkCode0
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representationsCode0
Towards a Sampling Theory for Implicit Neural Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified