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 1621–1630 of 3874 papers

TitleStatusHype
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural 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 Restoration using Autoencoding Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified