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 2621–2630 of 3874 papers

TitleStatusHype
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation—0
Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition KernelsCode0
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art—0
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution—0
Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation—0
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution—0
Resampling and super-resolution of hexagonally sampled images using deep learning—0
Blind inverse problems with isolated spikes—0
FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos—0
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified