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 2541–2550 of 3874 papers

TitleStatusHype
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network—0
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding—0
Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning—0
Simultaneous Super-Resolution of Depth and Images Using a Single Camera—0
SimUSR: A Simple but Strong Baseline for Unsupervised Image 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
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
Show:102550
← PrevPage 255 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified