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 1001–1010 of 3874 papers

TitleStatusHype
Towards Real Scene Super-Resolution with Raw ImagesCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven ApproachCode1
Towards Robust Scene Text Image Super-resolution via Explicit Location EnhancementCode1
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-ResolutionCode1
An efficient CNN for spectral reconstruction from RGB imagesCode1
EgoVSR: Towards High-Quality Egocentric Video Super-ResolutionCode1
EndoL2H: Deep Super-Resolution for Capsule EndoscopyCode1
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified