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 2751–2775 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified