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 3501–3525 of 3874 papers

TitleStatusHype
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
Learning Likelihoods with Conditional Normalizing FlowsCode0
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
Super-Resolution without High-Resolution Labels for Black Hole SimulationsCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Learning Descriptor Networks for 3D Shape Synthesis and AnalysisCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
Pixel Recursive Super ResolutionCode0
Learning Accurate and Enriched Features for Stereo Image Super-ResolutionCode0
Edge-guided and Cross-scale Feature Fusion Network for Efficient Multi-contrast MRI Super-ResolutionCode0
PLAIN: Scalable Estimation Architecture for Integrated Sensing and CommunicationCode0
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
SLVR: Super-Light Visual Reconstruction via Blueprint Controllable Convolutions and Exploring Feature Diversity RepresentationCode0
s-LWSR: Super Lightweight Super-Resolution NetworkCode0
LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance RegularizationCode0
Plug-and-Play Linear Attention for Pre-trained Image and Video Restoration ModelsCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-ResolutionCode0
Wavelet Flow: Fast Training of High Resolution Normalizing FlowsCode0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancementCode0
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal SurveillanceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified