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 471–480 of 3874 papers

TitleStatusHype
Fingerprints of Super Resolution Networks—0
Volumetric Conditioning Module to Control Pretrained Diffusion Models for 3D Medical ImagesCode1
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution—0
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
Super-resolution in disordered media using neural networks—0
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI—0
Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution NetworkCode1
Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-ResolutionCode0
Super-resolved virtual staining of label-free tissue using diffusion models—0
A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified