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 2851–2875 of 3874 papers

TitleStatusHype
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Learning to Learn to Compress—0
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging—0
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Video compression with low complexity CNN-based spatial resolution adaptation—0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent NetworksCode1
Efficient OCT Image Segmentation Using Neural Architecture Search—0
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-ResolutionCode0
Deep learning Framework for Mobile MicroscopyCode0
Solving Linear Inverse Problems Using the Prior Implicit in a DenoiserCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
Video Super Resolution Based on Deep Learning: A Comprehensive Survey—0
T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions—0
MuCAN: Multi-Correspondence Aggregation Network for Video Super-ResolutionCode1
Frequency Domain-based Perceptual Loss for Super ResolutionCode1
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
An Image Analogies Approach for Multi-Scale Contour Detection—0
Video Super-resolution with Temporal Group AttentionCode1
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Face Super-Resolution Guided by 3D Facial PriorsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified