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 3431–3440 of 3874 papers

TitleStatusHype
Perceptual deep depth super-resolutionCode0
Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging—0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
3DSRnet: Video Super-resolution using 3D Convolutional Neural NetworksCode0
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network—0
Efficient Super Resolution Using Binarized Neural Network—0
Advanced Super-Resolution using Lossless Pooling Convolutional Networks—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
Efficient Super Resolution For Large-Scale Images Using Attentional GAN—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified