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
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-ResolutionCode3
HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech SynthesisCode3
AudioSR: Versatile Audio Super-resolution at ScaleCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual ShiftingCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Activating More Pixels in Image Super-Resolution TransformerCode3
A new face swap method for image and video domains: a technical reportCode3
VRT: A Video Restoration TransformerCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified