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 1391–1400 of 3874 papers

TitleStatusHype
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Laplacian Pyramid-like AutoencoderCode0
Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-ResolutionCode0
Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-ResolutionCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
LAR-SR: A Local Autoregressive Model for Image Super-ResolutionCode0
Combination of Single and Multi-frame Image Super-resolution: An Analytical PerspectiveCode0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-ResolversCode0
Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion ModelsCode0
EnhanceNet: Single Image Super-Resolution Through Automated Texture SynthesisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified