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 1526–1550 of 3874 papers

TitleStatusHype
In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition—0
Learning multi-scale local conditional probability models of imagesCode1
Combination of Single and Multi-frame Image Super-resolution: An Analytical PerspectiveCode0
Super-Resolution Neural OperatorCode0
Stochastic Super-Resolution For Gaussian Textures—0
Single-photon Image Super-resolution via Self-supervised Learning—0
Consistency ModelsCode5
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution—0
Online Streaming Video Super-Resolution with Convolutional Look-Up Table—0
Lessons Learned Report: Super-Resolution for Detection Tasks in Engineering Problem-Solving—0
TextIR: A Simple Framework for Text-based Editable Image Restoration—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Learning to Super-Resolve Blurry Images with EventsCode1
LSR: A Light-Weight Super-Resolution Method—0
Spatially-Adaptive Feature Modulation for Efficient Image Super-ResolutionCode2
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded ImagesCode1
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuningCode1
On The Role of Alias and Band-Shift for Sentinel-2 Super-Resolution—0
DISCO: Distributed Inference with Sparse Communications—0
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified