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 2501–2550 of 3874 papers

TitleStatusHype
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer—0
Capsule GAN for Prostate MRI Super-Resolution—0
Multi-scale super-resolution generation of low-resolution scanned pathological imagesCode0
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical NaturalnessCode1
End-to-end Alternating Optimization for Blind Super ResolutionCode1
A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution—0
Multi-Resolution Data Fusion for Super Resolution ImagingCode0
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution—0
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial NetworksCode0
Analog Neural Computing with Super-resolution Memristor Crossbars—0
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images—0
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
Unsupervised Remote Sensing Super-Resolution via Migration Image PriorCode1
Infrared Image Super-Resolution via Transfer Learning and PSRGANCode1
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation—0
COMISR: Compression-Informed Video Super-ResolutionCode0
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics—0
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning—0
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsCode1
NTIRE 2021 Challenge on Video Super-Resolution—0
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and AlignmentCode3
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy—0
SRWarp: Generalized Image Super-Resolution under Arbitrary TransformationCode1
Temporal Modulation Network for Controllable Space-Time Video Super-ResolutionCode1
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging—0
A Two-Stage Attentive Network for Single Image Super-ResolutionCode1
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution—0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Attention in Attention Network for Image Super-ResolutionCode1
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Kernel Adversarial Learning for Real-world Image Super-resolution—0
VSpSR: Explorable Super-Resolution via Variational Sparse Representation—0
Multitask Learning for VVC Quality Enhancement and Super-Resolution—0
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Image Super-Resolution via Iterative RefinementCode1
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-ResolutionCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—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
Context-self contrastive pretraining for crop type semantic segmentationCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
NU-Wave: A Diffusion Probabilistic Model for Neural Audio UpsamplingCode1
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified