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 2841–2850 of 3874 papers

TitleStatusHype
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
A Wideband Distributed Massive MIMO Channel Sounder for Communication and Sensing—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
Show:102550
← PrevPage 285 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified