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 661–670 of 3874 papers

TitleStatusHype
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face RestorationCode1
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element NetworksCode1
Enriched CNN-Transformer Feature Aggregation Networks for Super-ResolutionCode1
STDAN: Deformable Attention Network for Space-Time Video Super-ResolutionCode1
Unfolded Deep Kernel Estimation for Blind Image Super-resolutionCode1
Rethinking data-driven point spread function modeling with a differentiable optical modelCode1
Learning the Degradation Distribution for Blind Image Super-ResolutionCode1
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for PansharpeningCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified