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 1451–1460 of 3874 papers

TitleStatusHype
Local-Global Temporal Difference Learning for Satellite Video Super-ResolutionCode1
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
Towards Realistic Ultrasound Fetal Brain Imaging SynthesisCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
Super-Resolving Face Image by Facial Parsing InformationCode0
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-ResolutionCode1
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified