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 3501–3510 of 3874 papers

TitleStatusHype
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super ResolutionCode0
Learning Likelihoods with Conditional Normalizing FlowsCode0
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
Super-Resolution without High-Resolution Labels for Black Hole SimulationsCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Boosting Lightweight Single Image Super-resolution via Joint-distillationCode0
Learning Descriptor Networks for 3D Shape Synthesis and AnalysisCode0
Learning a Single Convolutional Super-Resolution Network for Multiple DegradationsCode0
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
Learning a No-Reference Quality Metric for Single-Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified