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 1476–1500 of 3874 papers

TitleStatusHype
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face RecognitionCode0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
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
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing FlowCode0
FSRNet: End-to-End Learning Face Super-Resolution with Facial PriorsCode0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
Improved Pothole Detection Using YOLOv7 and ESRGANCode0
Improving Super-Resolution Methods via Incremental Residual LearningCode0
Implicit Image-to-Image Schrodinger Bridge for Image RestorationCode0
Efficient Event Stream Super-Resolution with Recursive Multi-Branch FusionCode0
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate DataCode0
cGANs with Projection DiscriminatorCode0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
CFSNet: Toward a Controllable Feature Space for Image RestorationCode0
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse CodingCode0
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected NetworkCode0
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific SimulationsCode0
Image Super-resolution via Feature-augmented Random ForestCode0
Image Super-Resolution via Deep Recursive Residual NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified