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 1441–1450 of 3874 papers

TitleStatusHype
Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution—0
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
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis—0
Hyperspectral Spatial Super-Resolution using Keystone Error—0
Advancements in Image Resolution: Super-Resolution Algorithm for Enhanced EOS-06 OCM-3 Data—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified