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 1831–1840 of 3874 papers

TitleStatusHype
Realistic Hair Synthesis with Generative Adversarial Networks—0
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality AssessmentCode0
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics—0
Magnitude-image based data-consistent deep learning method for MRI super resolution—0
Generative Adversarial Super-Resolution at the Edge with Knowledge DistillationCode1
Video Restoration with a Deep Plug-and-Play Prior—0
Deep filter bank regression for super-resolution of anisotropic MR brain images—0
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification—0
Quasi-supervised Learning for Super-resolution PETCode0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified