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 3001–3025 of 3874 papers

TitleStatusHype
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Learning to Learn to Compress—0
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions—0
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging—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
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-ResolutionCode0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
Deep learning Framework for Mobile MicroscopyCode0
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
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
An Image Analogies Approach for Multi-Scale Contour Detection—0
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution—0
Super-Resolution Remote Imaging using Time Encoded Remote Apertures—0
Learning with Privileged Information for Efficient Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified