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 2981–2990 of 3874 papers

TitleStatusHype
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Interpretable Deep Multimodal Image Super-Resolution—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Image Super-Resolution using Explicit Perceptual Loss—0
Accelerated WGAN update strategy with loss change rate balancing—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified