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 2901–2910 of 3874 papers

TitleStatusHype
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
A Two-Stage Rotation-Based Super-Resolution Signature Estimation for Spatial Wideband Systems—0
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—0
Unsupervised Video Understanding by Reconciliation of Posture Similarities—0
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution—0
Attention-based Image Upsampling—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging—0
ATGV-Net: Accurate Depth Super-Resolution—0
Rethinking Image Evaluation in Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified