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 311–320 of 3874 papers

TitleStatusHype
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal LearningCode1
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid DynamicsCode1
Catch-A-Waveform: Learning to Generate Audio from a Single Short ExampleCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Cascaded Temporal Updating Network for Efficient Video Super-ResolutionCode1
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-ResolutionCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified