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 861–870 of 3874 papers

TitleStatusHype
Rethinking data-driven point spread function modeling with a differentiable optical modelCode1
Learning A Single Network for Scale-Arbitrary Super-ResolutionCode1
Bayesian Image Reconstruction using Deep Generative ModelsCode1
Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolutionCode1
Learning Large-Factor EM Image Super-Resolution with Generative PriorsCode1
Image Super-Resolution with Deep DictionaryCode1
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars MiningCode1
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-ResolutionCode1
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified