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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2371–2380 of 3874 papers

TitleStatusHype
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model—0
Hybrid attention structure preserving network for reconstruction of under-sampled OCT images—0
Hybrid Neural Representations for Spherical Data—0
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution—0
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks—0
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications—0
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified