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 1291–1300 of 3874 papers

TitleStatusHype
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Depth Separable architecture for Sentinel-5P Super-Resolution—0
MR imaging in the low-field: Leveraging the power of machine learning—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
Compensation based Dictionary Transfer for Similar Multispectral Image Spectral Super-resolution—0
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI—0
Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural OperatorsCode0
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual InferenceCode0
Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?—0
Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified