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 2971–2980 of 3874 papers

TitleStatusHype
Blind Image Super-Resolution with Spatial Context Hallucination—0
Reconstruct high-resolution multi-focal plane images from a single 2D wide field image—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement—0
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing—0
Understanding Deformable Alignment in Video Super-Resolution—0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified