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 1701–1710 of 3874 papers

TitleStatusHype
Space-Time Video Super-resolution with Neural Operator—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
Gull: A Generative Multifunctional Audio Codec—0
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint—0
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions—0
Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
Translation-based Video-to-Video Synthesis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified