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 1901–1910 of 3874 papers

TitleStatusHype
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network—0
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
Deep learning in ultrasound imaging—0
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution—0
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces—0
HypervolGAN: An efficient approach for GAN with multi-objective training function—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified