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 2341–2350 of 3874 papers

TitleStatusHype
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
The End Restraint Method for Mechanically Perturbing Nucleic Acids in silico—0
Super-Resolution Appearance Transfer for 4D Human Performances—0
Attention-based Multi-Reference Learning for Image Super-ResolutionCode0
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution—0
Self-Attention for Audio Super-ResolutionCode1
Multi-Attributed and Structured Text-to-Face Synthesis—0
Generalized Real-World Super-Resolution through Adversarial RobustnessCode1
Transformer for Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified