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

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Showing 3101–3110 of 3874 papers

TitleStatusHype
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning—0
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data—0
RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling Based Video Coding—0
3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution—0
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-ResolutionCode0
Generator From Edges: Reconstruction of Facial Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified