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 3826–3850 of 3874 papers

TitleStatusHype
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency—0
The Domain Transform Solver—0
Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution—0
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
Guided Frequency Loss for Image Restoration—0
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution—0
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
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Handling Motion Blur in Multi-Frame Super-Resolution—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation—0
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging—0
The End Restraint Method for Mechanically Perturbing Nucleic Acids in silico—0
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified