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 2451–2475 of 3874 papers

TitleStatusHype
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
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction—0
Neural RF SLAM for unsupervised positioning and mapping with channel state information—0
Combining Transformer Generators with Convolutional Discriminators—0
Combined Generative and Predictive Modeling for Speech Super-resolution—0
Combined Channel and Spatial Attention-based Stereo Endoscopic Image Super-Resolution—0
Neural Volume Super-Resolution—0
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review—0
Neuromorphic Imaging with Super-Resolution—0
CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation—0
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network—0
Neutron Ghost Imaging—0
New Algorithms for Learning Incoherent and Overcomplete Dictionaries—0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
New wavelet-based superresolution algorithm for speckle reduction in SAR images—0
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
NLCUnet: Single-Image Super-Resolution Network with Hairline Details—0
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems—0
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy—0
Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes—0
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution—0
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise—0
Non-convex Super-resolution of OCT images via sparse representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified