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 1071–1080 of 3874 papers

TitleStatusHype
PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-ResolutionCode1
Zooming Out on Zooming In: Advancing Super-Resolution for Remote SensingCode2
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
Neural Fields with Thermal Activations for Arbitrary-Scale Super-ResolutionCode2
Super-Resolution through StyleGAN Regularized Latent Search: A Realism-Fidelity Trade-off—0
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain ImagingCode1
High-resolution Multi-spectral Image Guided DEM Super-resolution using Sinkhorn Regularized Adversarial Network—0
SeeSR: Towards Semantics-Aware Real-World Image Super-ResolutionCode4
LFSRDiff: Light Field Image Super-Resolution via Diffusion ModelsCode1
CoSeR: Bridging Image and Language for Cognitive Super-ResolutionCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified