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 1576–1600 of 3874 papers

TitleStatusHype
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study—0
Structure Flow-Guided Network for Real Depth Super-Resolution—0
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel—0
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows—0
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 imagesCode0
Simple diffusion: End-to-end diffusion for high resolution imagesCode2
Trainable Loss Weights in Super-ResolutionCode0
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?Code0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution—0
AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics—0
Super-Resolution Harmonic Retrieval of Non-Circular Signals—0
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Learning Continuous Mesh Representation with Spherical Implicit SurfaceCode1
Deep Residual Axial Networks—0
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model—0
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging—0
Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening FrameworkCode0
WIRE: Wavelet Implicit Neural RepresentationsCode1
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution—0
Super-resolution with Sparse Arrays: A Non-Asymptotic Analysis of Spatio-temporal Trade-offs—0
Super-resolution with Binary Priors: Theory and Algorithms—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified