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 3576–3600 of 3874 papers

TitleStatusHype
Joint Maximum Purity Forest with Application to Image Super-ResolutionCode0
Joint High Dynamic Range Imaging and Super-Resolution from a Single ImageCode0
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRICode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
IterInv: Iterative Inversion for Pixel-Level T2I ModelsCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face RecognitionCode0
Inverse Problems with Diffusion Models: A MAP Estimation PerspectiveCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-ResolutionCode0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
DeepCEL0 for 2D Single Molecule Localization in Fluorescence MicroscopyCode0
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing ImagesCode0
Distortion-aware super-resolution for planetary exploration imagesCode0
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep LearningCode0
Spatially-Variant Degradation Model for Dataset-free Super-resolutionCode0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Blind Image Fusion for Hyperspectral Imaging with the Directional Total VariationCode0
Quasi-supervised Learning for Super-resolution PETCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified