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 2801–2850 of 3874 papers

TitleStatusHype
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical ZoomCode0
MR Slice Profile Estimation by Learning to Match Internal Patch DistributionsCode0
Near field Acoustic Holography on arbitrary shapes using Convolutional Neural NetworkCode0
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos—0
Omniscient Video Super-Resolution—0
In-Place Scene Labelling and Understanding with Implicit Scene Representation—0
Training a Task-Specific Image Reconstruction Loss—0
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution—0
Multi-frame Super-resolution from Noisy Data—0
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy—0
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution—0
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network—0
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling—0
Gauging diffraction patterns: field of view and bandwidth estimation in lensless holography—0
Mathematical Theory of Computational Resolution Limit in Multi-dimensions—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
Prediction-assistant Frame Super-Resolution for Video Streaming—0
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution—0
D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution—0
A learning-based view extrapolation method for axial super-resolution—0
Super-Resolving Beyond Satellite Hardware Using Realistically Degraded Images—0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Deep learning-based super-resolution fluorescence microscopy on small datasetsCode0
Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources—0
Super-Resolution DOA Estimation for Wideband Signals using Arbitrary Linear Arrays without Focusing Matrices—0
Provable Compressed Sensing with Generative Priors via Langevin Dynamics—0
Learning for Unconstrained Space-Time Video Super-Resolution—0
ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning—0
Deep Unrolled Network for Video Super-Resolution—0
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution—0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Mobile Computational Photography: A Tour—0
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images—0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified