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 3001–3050 of 3874 papers

TitleStatusHype
Single Pair Cross-Modality Super Resolution—0
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks—0
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning—0
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration—0
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization—0
What makes for good morphology representations for spatial omics?—0
What's Behind the Mask: Estimating Uncertainty in Image-to-Image Problems—0
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
When Geoscience Meets Generative AI and Large Language Models: Foundations, Trends, and Future Challenges—0
When Super-Resolution Meets Camouflaged Object Detection: A Comparison Study—0
When to Use Convolutional Neural Networks for Inverse Problems—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data—0
W-Net: A Facial Feature-Guided Face Super-Resolution Network—0
XAI-based gait analysis of patients walking with Knee-Ankle-Foot orthosis using video cameras—0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
XCycles Backprojection Acoustic Super-Resolution—0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—0
YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO—0
You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priors—0
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution—0
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
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