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 2121–2130 of 3874 papers

TitleStatusHype
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-ResolutionCode0
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients—0
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
A High-Frequency Focused Network for Lightweight Single Image Super-Resolution—0
CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified