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 2361–2370 of 3874 papers

TitleStatusHype
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification—0
Quasi-supervised Learning for Super-resolution PETCode0
ELSR: Extreme Low-Power Super Resolution Network For Mobile DevicesCode0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noiseCode0
Laplacian Pyramid-like AutoencoderCode0
3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming—0
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
Time-lapse image classification using a diffractive neural network—0
Towards Robust Drone Vision in the Wild—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified