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 3721–3730 of 3874 papers

TitleStatusHype
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes—0
S^3Mamba: Arbitrary-Scale Super-Resolution via Scaleable State Space Model—0
ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar—0
Effect of structure-based training on 3D localization precision and quality—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks—0
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images—0
Functional Neural Networks for Parametric Image Restoration Problems—0
Functional Nonlinear Sparse Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified