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 2251–2260 of 3874 papers

TitleStatusHype
Advancing High-Resolution Video-Language Representation with Large-Scale Video TranscriptionsCode1
Image Super-Resolution Using T-Tetromino Pixels—0
Local Texture Estimator for Implicit Representation FunctionCode1
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution—0
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution—0
Pansharpening by convolutional neural networks in the full resolution frameworkCode1
Image-specific Convolutional Kernel Modulation for Single Image Super-resolutionCode1
Pixel-Level Kernel Estimation for Blind Super-ResolutionCode0
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal SurveillanceCode0
Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified