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 3661–3670 of 3874 papers

TitleStatusHype
Structured illumination microscopy for dual-modality 3D sub-diffraction resolution fluorescence and refractive-index reconstruction—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
MemNet: A Persistent Memory Network for Image RestorationCode0
Unsupervised Video Understanding by Reconciliation of Posture Similarities—0
Audio Super Resolution using Neural NetworksCode0
Real-time Deep Video DeinterlacingCode0
Depth Super-Resolution Meets Uncalibrated Photometric StereoCode0
A Framework for Super-Resolution of Scalable Video via Sparse Reconstruction of Residual Frames—0
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning—0
A unified method for super-resolution recovery and real exponential-sum separation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified