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 121–130 of 3874 papers

TitleStatusHype
One Target, Many Views: Multi-User Fusion for Collaborative Uplink ISAC—0
GuideSR: Rethinking Guidance for One-Step High-Fidelity Diffusion-Based Super-Resolution—0
Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution—0
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional NetworkCode0
SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution—0
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation—0
Super-resolution Wideband Beam Training for Near-field Communications with Ultra-low Overhead—0
Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information—0
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials—0
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified