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 3821–3830 of 3874 papers

TitleStatusHype
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution—0
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
Gridless Parameter Estimation in Partly Calibrated Rectangular Arrays—0
Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency—0
The Domain Transform Solver—0
Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution—0
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified