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 631–640 of 3874 papers

TitleStatusHype
Deep Unfolding Network for Image Super-ResolutionCode1
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and ReconstructionCode1
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional FlowsCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Guided Super-Resolution as Pixel-to-Pixel TransformationCode1
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion ModuleCode1
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-ResolutionCode1
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-ResolutionCode1
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image FusionCode1
CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed TomographyCode1
Show:102550
← PrevPage 64 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified