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 3101–3125 of 3874 papers

TitleStatusHype
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution—0
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses—0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder—0
Single Image Super-Resolution—0
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution—0
Very Deep Super-Resolution of Remotely Sensed Images with Mean Square Error and Var-norm Estimators as Loss Functions—0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
Single Image Super-Resolution Based on Global-Local Information Synergy—0
Single Image Super-Resolution based on Wiener Filter in Similarity Domain—0
Single image super-resolution by approximated Heaviside functions—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
Single Image Super-Resolution From Transformed Self-Exemplars—0
Single image super resolution in spatial and wavelet domain—0
Single Image Super-Resolution Methods: A Survey—0
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels—0
Single Image Super-Resolution Using Lightweight CNN with Maxout Units—0
Single Image Super-resolution using Deformable Patches—0
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer—0
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network—0
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion—0
Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction—0
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network—0
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term—0
An Image Analogies Approach for Multi-Scale Contour Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified