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 1551–1575 of 3874 papers

TitleStatusHype
First order algorithms in variational image processing—0
Astronomical Image Colorization and upscaling with Generative Adversarial Networks—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
Geometric Distortion Guided Transformer for Omnidirectional Image Super-Resolution—0
FIPER: Generalizable Factorized Features for Robust Low-Level Vision Models—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Geometry Enhancements from Visual Content: Going Beyond Ground Truth—0
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
Fingerprints of Super Resolution Networks—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations—0
Fingerprinting Deep Image Restoration Models—0
Advancing Supervised Local Learning Beyond Classification with Long-term Feature Bank—0
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
A Coordinate Descent Approach to Atomic Norm Denoising—0
2D Neural Fields with Learned Discontinuities—0
Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization—0
Hyperspectral Neural Radiance Fields—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping—0
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution—0
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified