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 2501–2525 of 3874 papers

TitleStatusHype
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
Optical Flow for Video Super-Resolution: A Survey—0
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution—0
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled Image Data Using Locally Fully Connected LayersCode0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
Image Super-Resolution With Deep Variational Autoencoders—0
Towards True Detail Restoration for Super-Resolution: A Benchmark and a Quality Metric—0
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
Sub-Terahertz Channel Measurements and Characterization in a Factory Building—0
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
Depth-Independent Depth Completion via Least Square Estimation—0
A Novel Dual Dense Connection Network for Video Super-resolution—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence—0
Enhanced Image Reconstruction From Quarter Sampling Measurements Using An Adapted Very Deep Super Resolution Network—0
Fine-grained Urban Flow Inference with Incomplete DataCode0
Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating—0
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU—0
Multi-image Super-resolution via Quality Map Associated Attention Network—0
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified