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 3151–3160 of 3874 papers

TitleStatusHype
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop—0
HyperCon: Image-To-Video Model Transfer for Video-To-Video Translation Tasks—0
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Cross-Resolution Learning for Face RecognitionCode0
Explorable Super ResolutionCode0
High-quality Speech Synthesis Using Super-resolution Mel-Spectrogram—0
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified