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 2161–2170 of 3874 papers

TitleStatusHype
Facial Attribute Capsules for Noise Face Super Resolution—0
Facial Expression Restoration Based on Improved Graph Convolutional Networks—0
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Fair Primal Dual Splitting Method for Image Inverse Problems—0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization—0
FAN: Frequency Aggregation Network for Real Image Super-resolution—0
FarSee-Net: Real-Time Semantic Segmentation by Efficient Multi-scale Context Aggregation and Feature Space Super-resolution—0
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified