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 1521–1530 of 3874 papers

TitleStatusHype
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Provably Convergent Plug-and-Play Quasi-Newton MethodsCode0
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-ResolutionCode1
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-ResolutionCode1
EfficientTempNet: Temporal Super-Resolution of Radar Rainfall—0
In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks—0
QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms—0
Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition—0
Learning multi-scale local conditional probability models of imagesCode1
Combination of Single and Multi-frame Image Super-resolution: An Analytical PerspectiveCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified