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 2776–2800 of 3874 papers

TitleStatusHype
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
AIM 2020 Challenge on Video Temporal Super-Resolution—0
High-throughput molecular imaging via deep learning enabled Raman spectroscopyCode1
Interpretable Detail-Fidelity Attention Network for Single Image Super-ResolutionCode1
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar—0
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution—0
Blind Image Super-Resolution with Spatial Context Hallucination—0
Tarsier: Evolving Noise Injection in Super-Resolution GANsCode1
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results—0
Deep Artifact-Free Residual Network for Single Image Super-Resolution—0
Residual Feature Distillation Network for Lightweight Image Super-ResolutionCode1
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain ConnectomesCode1
Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change ProjectionsCode1
Local-Global Fusion Network for Video Super-ResolutionCode1
NEMO: enabling neural-enhanced video streaming on commodity mobile devicesCode1
Reconstruct high-resolution multi-focal plane images from a single 2D wide field image—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Searching for Low-Bit Weights in Quantized Neural NetworksCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing—0
AIM 2020 Challenge on Efficient Super-Resolution: Methods and ResultsCode2
Understanding Deformable Alignment in Video Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified