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 2931–2940 of 3874 papers

TitleStatusHype
Unpaired MRI Super Resolution with Contrastive Learning—0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression—0
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Unsupervised Learning for Real-World Super-Resolution—0
Show:102550
← PrevPage 294 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified