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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3026–3050 of 3874 papers

TitleStatusHype
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy—0
ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network—0
Zoom in to the details of human-centric videos—0
Zoom to Learn, Learn to Zoom—0
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices—0
Neural Operators for Accelerating Scientific Simulations and Design—0
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs—0
Neural Prior for Trajectory Estimation—0
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Neural Volume Super-Resolution—0
Neuromorphic Imaging with Super-Resolution—0
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
Neutron Ghost Imaging—0
New Algorithms for Learning Incoherent and Overcomplete Dictionaries—0
New wavelet-based superresolution algorithm for speckle reduction in SAR images—0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details—0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy—0
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise—0
Non-convex Super-resolution of OCT images via sparse representation—0
Nondestructive thermographic detection of internal defects using pixel-pattern based laser excitation and photothermal super resolution reconstruction—0
Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning—0
PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals—0
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution—0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified