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 1471–1480 of 3874 papers

TitleStatusHype
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring—0
A Generative Adversarial Network for AI-Aided Chair Design—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Frequency-Time Diffusion with Neural Cellular Automata—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified