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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2901–2925 of 3874 papers

TitleStatusHype
RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images—0
A Two-Stage Rotation-Based Super-Resolution Signature Estimation for Spatial Wideband Systems—0
A two-stage 3D Unet framework for multi-class segmentation on full resolution image—0
Unsupervised Video Understanding by Reconciliation of Posture Similarities—0
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution—0
Attention-based Image Upsampling—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging—0
ATGV-Net: Accurate Depth Super-Resolution—0
Rethinking Image Evaluation in Super-Resolution—0
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold—0
Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective—0
Rethinking Implicit Neural Representations for Vision Learners—0
A Survey on Super Resolution for video Enhancement Using GAN—0
Rethinking Super-Resolution as Text-Guided Details Generation—0
A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation—0
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution—0
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution—0
A Survey on Deep learning based Document Image Enhancement—0
Revealing economic facts: LLMs know more than they say—0
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report—0
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
Revisiting L1 Loss in Super-Resolution: A Probabilistic View and Beyond—0
A survey of machine learning-based physics event generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified