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 2261–2270 of 3874 papers

TitleStatusHype
Temporal Super-Resolution using Multi-Channel Illumination Source—0
A mathematical theory of resolution limits for super-resolution of positive sources—0
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time—0
Rethinking Implicit Neural Representations for Vision Learners—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution—0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images—0
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network—0
Stereo Image Rain Removal via Dual-View Mutual Attention—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified