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 301–310 of 3874 papers

TitleStatusHype
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
A heterogeneous group CNN for image super-resolutionCode1
Adaptive Convolutional Neural Network for Image Super-resolutionCode1
Catch-A-Waveform: Learning to Generate Audio from a Single Short ExampleCode1
Action Matching: Learning Stochastic Dynamics from SamplesCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal LearningCode1
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified