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 1451–1460 of 3874 papers

TitleStatusHype
AnyTSR: Any-Scale Thermal Super-Resolution for UAVCode0
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face RecognitionCode0
Inverse Problems with Diffusion Models: A MAP Estimation PerspectiveCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
City Scene Super-Resolution via Geometric Error MinimizationCode0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
CISRNet: Compressed Image Super-Resolution NetworkCode0
3D Appearance Super-Resolution with Deep LearningCode0
Kernel-aware Burst Blind Super-ResolutionCode0
Efficient Single Image Super Resolution using Enhanced Learned Group ConvolutionsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified