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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1625 of 3874 papers

TitleStatusHype
Gull: A Generative Multifunctional Audio Codec—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System—0
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Handling Motion Blur in Multi-Frame Super-Resolution—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Progressive Image Super-Resolution via Neural Differential Equation—0
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging—0
A Unified Plug-and-Play Algorithm with Projected Landweber Operator for Split Convex Feasibility Problems—0
Fine-Grained Neural Architecture Search—0
A statistically constrained internal method for single image super-resolution—0
A Convex Approach for Image Hallucination—0
Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Image Restoration using Autoencoding Priors—0
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution—0
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified