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 2351–2375 of 3874 papers

TitleStatusHype
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction—0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
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
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
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
Trustworthy modelling of atmospheric formaldehyde powered by deep learning—0
Extreme-scale Talking-Face Video Upsampling with Audio-Visual PriorsCode0
Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI—0
Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA—0
An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of MarsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified