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 1451–1475 of 3874 papers

TitleStatusHype
Fourier Neural Operator based surrogates for CO_2 storage in realistic geologies—0
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data—0
Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution—0
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions—0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
A survey of machine learning-based physics event generation—0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—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
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—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
FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring—0
A Generative Adversarial Network for AI-Aided Chair Design—0
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution—0
Frequency Consistent Adaptation for Real World Super Resolution—0
Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
A super-resolution reconstruction method for lightweight building images based on an expanding feature modulation network—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified