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 1871–1880 of 3874 papers

TitleStatusHype
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—0
Content-Aware Local GAN for Photo-Realistic Super-Resolution—0
InverseNet: Solving Inverse Problems with Splitting Networks—0
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution—0
Facial Expression Restoration Based on Improved Graph Convolutional Networks—0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified