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 221–230 of 3874 papers

TitleStatusHype
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstructionCode1
SASNet: Spatially-Adaptive Sinusoidal Neural Networks—0
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation—0
Resolution Invariant Autoencoder—0
MegaSR: Mining Customized Semantics and Expressive Guidance for Image Super-ResolutionCode1
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
Over-the-Air Time-Frequency Synchronization in Distributed ISAC Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified