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 3711–3720 of 3874 papers

TitleStatusHype
Efficient Channel Estimation for Millimeter Wave and Terahertz Systems Enabled by Integrated Super-resolution Sensing and Communication—0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
Advanced Super-Resolution using Lossless Pooling Convolutional Networks—0
Frequency-Time Diffusion with Neural Cellular Automata—0
Efficient and Phase-aware Video Super-resolution for Cardiac MRI—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
From General to Specific: Online Updating for Blind Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified