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 371–380 of 3874 papers

TitleStatusHype
Dual Super-Resolution Learning for Semantic SegmentationCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-ResolutionCode1
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
A Tree-guided CNN for image super-resolutionCode1
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data CharacteristicCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified