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 41–50 of 3874 papers

TitleStatusHype
Event-Enhanced Blurry Video Super-ResolutionCode3
BasicVSR: The Search for Essential Components in Video Super-Resolution and BeyondCode3
Activating More Pixels in Image Super-Resolution TransformerCode3
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech SynthesisCode3
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion TransformerCode3
AudioSR: Versatile Audio Super-resolution at ScaleCode3
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
A new face swap method for image and video domains: a technical reportCode3
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-ResolutionCode3
Degradation-Guided One-Step Image Super-Resolution with Diffusion PriorsCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified