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 2201–2250 of 3874 papers

TitleStatusHype
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
Fine-Grained Neural Architecture Search—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Fingerprinting Deep Image Restoration Models—0
Fingerprints of Super Resolution Networks—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
First order algorithms in variational image processing—0
FlashSR: One-step Versatile Audio Super-resolution via Diffusion Distillation—0
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution—0
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration—0
FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring—0
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions—0
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data—0
Fourier Neural Operator based surrogates for CO_2 storage in realistic geologies—0
Fourier Space Losses for Efficient Perceptual Image Super-Resolution—0
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation—0
FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution—0
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration—0
Frame and Feature-Context Video Super-Resolution—0
Frame-Recurrent Video Super-Resolution—0
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution—0
FREGAN : an application of generative adversarial networks in enhancing the frame rate of videos—0
FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss—0
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform—0
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network—0
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution—0
Frequency Consistent Adaptation for Real World Super Resolution—0
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution—0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
Frequency-Time Diffusion with Neural Cellular Automata—0
From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection with Super Resolution—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
From General to Specific: Online Updating for Blind Super-Resolution—0
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks—0
Functional Neural Networks for Parametric Image Restoration Problems—0
Functional Nonlinear Sparse Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified