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 1451–1475 of 3874 papers

TitleStatusHype
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
IterInv: Iterative Inversion for Pixel-Level T2I ModelsCode0
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face RecognitionCode0
Patch-based Progressive 3D Point Set UpsamplingCode0
City Scene Super-Resolution via Geometric Error MinimizationCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
CISRNet: Compressed Image Super-Resolution NetworkCode0
3D Appearance Super-Resolution with Deep LearningCode0
Inverse Problems with Diffusion Models: A MAP Estimation PerspectiveCode0
Efficient Single Image Super Resolution using Enhanced Learned Group ConvolutionsCode0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Improving Super-Resolution Methods via Incremental Residual LearningCode0
Efficient Deep Neural Network for Photo-realistic Image Super-ResolutionCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
Improved Pothole Detection Using YOLOv7 and ESRGANCode0
Advancing Super-Resolution in Neural Radiance Fields via Variational Diffusion StrategiesCode0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
Efficient Light Field Reconstruction via Spatio-Angular Dense NetworkCode0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified