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 971–980 of 3874 papers

TitleStatusHype
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-ResolutionCode1
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-ResolutionCode1
Local Implicit Wavelet Transformer for Arbitrary-Scale Super-ResolutionCode1
Local Texture Estimator for Implicit Representation FunctionCode1
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference ModelingCode1
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target GenerationCode1
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified