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 2061–2070 of 3874 papers

TitleStatusHype
Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution—0
Efficient Multi-disparity Transformer for Light Field Image Super-resolution—0
Efficient Multi-Purpose Cross-Attention Based Image Alignment Block for Edge Devices—0
Efficient neural supersampling on a novel gaming dataset—0
Efficient OCT Image Segmentation Using Neural Architecture Search—0
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation—0
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—0
Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration—0
Efficient Super Resolution For Large-Scale Images Using Attentional GAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified