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 2841–2850 of 3874 papers

TitleStatusHype
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC—0
Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks—0
Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective—0
Time accelerated image super-resolution using shallow residual feature representative network—0
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification—0
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
Time-lapse image classification using a diffractive neural network—0
Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks—0
SPIRE: Semantic Prompt-Driven Image Restoration—0
TMSR: Tiny Multi-path CNNs for Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified