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 1861–1870 of 3874 papers

TitleStatusHype
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
Interactive Image Manipulation with Complex Text Instructions—0
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Interpretable Deep Multimodal Image Super-Resolution—0
Content-Aware Local GAN for Photo-Realistic Super-Resolution—0
Interpretable Super-Resolution via a Learned Time-Series Representation—0
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified