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 2551–2575 of 3874 papers

TitleStatusHype
Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation—0
Optical Flow for Video Super-Resolution: A Survey—0
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution—0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Optimal Physical Preprocessing for Example-Based Super-Resolution—0
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space—0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
Optimal Transport for Super Resolution Applied to Astronomy Imaging—0
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics—0
Optimization of Module Transferability in Single Image Super-Resolution: Universality Assessment and Cycle Residual Blocks—0
Optimizing Drug Delivery in Smart Pharmacies: A Novel Framework of Multi-Stage Grasping Network Combined with Adaptive Robotics Mechanism—0
Optimizing Fingerprint-Spectrum-Based Synchronization in Integrated Sensing and Communications—0
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization—0
Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
ΩSFormer: Dual-Modal Ω-like Super-Resolution Transformer Network for Cross-scale and High-accuracy Terraced Field Vectorization Extraction—0
Cascade Convolutional Neural Network for Image Super-Resolution—0
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU—0
Uncertainty Estimation for Super-Resolution using ESRGAN—0
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence—0
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement—0
Over-the-Air Time-Frequency Synchronization in Distributed ISAC Systems—0
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model—0
Padding-free Convolution based on Preservation of Differential Characteristics of Kernels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified