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 3801–3810 of 3874 papers

TitleStatusHype
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Shepard Convolutional Neural NetworksCode0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution—0
Convolutional Sparse Coding for Image Super-Resolution—0
Naive Bayes Super-Resolution Forest—0
Example-Based Modeling of Facial Texture From Deficient Data—0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Learning to Generate Images with Perceptual Similarity Metrics—0
Super-Resolution with Deep Convolutional Sufficient StatisticsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified