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 2511–2520 of 3874 papers

TitleStatusHype
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with Decodability of Image Patches—0
Fast and selective super-resolution ultrasound in vivo with sono-switchable nanodroplets—0
Sub-Terahertz Channel Measurements and Characterization in a Factory Building—0
Regularized Training of Intermediate Layers for Generative Models for Inverse ProblemsCode0
Depth-Independent Depth Completion via Least Square Estimation—0
A Novel Dual Dense Connection Network for Video Super-resolution—0
Fast Neural Architecture Search for Lightweight Dense Prediction Networks—0
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence—0
Enhanced Image Reconstruction From Quarter Sampling Measurements Using An Adapted Very Deep Super Resolution Network—0
Fine-grained Urban Flow Inference with Incomplete DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified