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 3521–3530 of 3874 papers

TitleStatusHype
CT-image Super Resolution Using 3D Convolutional Neural Network—0
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?—0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN—0
Super-Resolution using Convolutional Neural Networks without Any Checkerboard ArtifactsCode0
Non-Local Recurrent Network for Image RestorationCode0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images—0
Mesoscopic Facial Geometry Inference Using Deep Neural Networks—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified