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 251–260 of 3874 papers

TitleStatusHype
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical SystemsCode1
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Catch-A-Waveform: Learning to Generate Audio from a Single Short ExampleCode1
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified