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 2531–2540 of 3874 papers

TitleStatusHype
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Attention in Attention Network for Image Super-ResolutionCode1
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Kernel Adversarial Learning for Real-world Image Super-resolution—0
VSpSR: Explorable Super-Resolution via Variational Sparse Representation—0
Multitask Learning for VVC Quality Enhancement and Super-Resolution—0
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Image Super-Resolution via Iterative RefinementCode1
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified