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 1641–1650 of 3874 papers

TitleStatusHype
AIM 2020 Challenge on Video Temporal Super-Resolution—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Coupled-Projection Residual Network for MRI Super-Resolution—0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Image Super-Resolution With Deep Variational Autoencoders—0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified