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 1691–1700 of 3874 papers

TitleStatusHype
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy—0
Blind Restoration of High-Resolution Ultrasound Video—0
Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling—0
Device Activity Detection and Channel Estimation for Millimeter-Wave Massive MIMO—0
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding—0
Analysis Operator Learning and Its Application to Image Reconstruction—0
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning—0
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision—0
Developing a new biophysical tool to combine magneto-optical tweezers with super-resolution fluorescence microscopy—0
Deterministic Medical Image Translation via High-fidelity Brownian Bridges—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified