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 51–60 of 3874 papers

TitleStatusHype
Activating More Pixels in Image Super-Resolution TransformerCode3
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and AlignmentCode3
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature TransformCode3
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
Panacea+: Panoramic and Controllable Video Generation for Autonomous DrivingCode3
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge ReportCode3
Degradation-Guided One-Step Image Super-Resolution with Diffusion PriorsCode3
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-ResolutionCode2
Denoising Diffusion Restoration ModelsCode2
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified