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 3101–3150 of 3874 papers

TitleStatusHype
ΩSFormer: Dual-Modal Ω-like Super-Resolution Transformer Network for Cross-scale and High-accuracy Terraced Field Vectorization Extraction—0
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU—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
Padding-free Convolution based on Preservation of Differential Characteristics of Kernels—0
PAG-Net: Progressive Attention Guided Depth Super-resolution Network—0
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening—0
Panoramas from Photons—0
PAON: A New Neuron Model using Padé Approximants—0
ParaDiS: Parallelly Distributable Slimmable Neural Networks—0
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
Parallel compressive super-resolution imaging with wide field-of-view based on physics enhanced network—0
Parallel Statistical Multi-resolution Estimation—0
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Particle-Filtering-based Latent Diffusion for Inverse Problems—0
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images—0
Patch-based image Super Resolution using generalized Gaussian mixture model—0
PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution—0
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution—0
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation—0
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment—0
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models—0
Perception-Oriented Stereo Image Super-Resolution—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Perceptual Fairness in Image Restoration—0
Perceptually-inspired super-resolution of compressed videos—0
Perceptually Optimized Super Resolution—0
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets—0
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
PFStorer: Personalized Face Restoration and Super-Resolution—0
PG-DPIR: An efficient plug-and-play method for high-count Poisson-Gaussian inverse problems—0
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction—0
Phase Retrieval using Untrained Neural Network Priors—0
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation—0
Photorealistic Video Generation with Diffusion Models—0
Perceptual Video Super Resolution with Enhanced Temporal Consistency—0
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging—0
Physics-informed deep-learning applications to experimental fluid mechanics—0
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models—0
Physics-Informed Super-Resolution Diffusion for 6D Phase Space Diagnostics—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration—0
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study—0
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report—0
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting—0
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution—0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified