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 71–80 of 3874 papers

TitleStatusHype
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction—0
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-ResolutionCode2
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
Super-Resolution with Structured Motion—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors—0
Blind Restoration of High-Resolution Ultrasound Video—0
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified