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 2921–2930 of 3874 papers

TitleStatusHype
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Underwater litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified