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 1861–1870 of 3874 papers

TitleStatusHype
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy—0
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Generalized super-resolution 4D Flow MRI x2013 using ensemble learning to extend across the cardiovascular systemCode0
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution—0
LATIS: Lambda Abstraction-based Thermal Image Super-resolution—0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution—0
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified