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 1041–1050 of 3874 papers

TitleStatusHype
Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements—0
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging—0
Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning PriorCode0
TULIP: Transformer for Upsampling of LiDAR Point CloudsCode1
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-ResolutionCode3
Precipitation Downscaling with Spatiotemporal Video Diffusion—0
Photorealistic Video Generation with Diffusion Models—0
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-ResolutionCode0
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-ResolutionCode1
Transformer-based Selective Super-Resolution for Efficient Image RefinementCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified