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 2751–2800 of 3874 papers

TitleStatusHype
Quantifying Climate Change Impacts on Renewable Energy Generation: A Super-Resolution Recurrent Diffusion Model—0
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception—0
Quantum Annealing for Single Image Super-Resolution—0
Quasi-Newton OMP Approach for Super-Resolution Channel Estimation and Extrapolation—0
Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features—0
Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model—0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
Radar Accurate Localization of UAV Swarms Based on Range Super-Resolution Method—0
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Bi-GANs-ST for Perceptual Image Super-resolution—0
Raising The Limit Of Image Rescaling Using Auxiliary Encoding—0
RAISR: Rapid and Accurate Image Super Resolution—0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
Bias for Action: Video Implicit Neural Representations with Bias Modulation—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation—0
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
Rate-Distortion Optimized Post-Training Quantization for Learned Image Compression—0
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio—0
RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution—0
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
RDRN: Recursively Defined Residual Network for Image Super-Resolution—0
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images—0
Beyond MR Image Harmonization: Resolution Matters Too—0
Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network—0
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection—0
Real Image Super-Resolution using GAN through modeling of LR and HR process—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Realistic Hair Synthesis with Generative Adversarial Networks—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution—0
BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Real-time, low-cost multi-person 3D pose estimation—0
Real-Time Neural-Enhancement for Online Cloud Gaming—0
Real-Time Neural Video Recovery and Enhancement on Mobile Devices—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Real-Time Super-Resolution for Real-World Images on Mobile Devices—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified