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 1601–1650 of 3874 papers

TitleStatusHype
Gull: A Generative Multifunctional Audio Codec—0
GUN: Gradual Upsampling Network for Single Image Super-Resolution—0
H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System—0
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution—0
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck—0
Handling Motion Blur in Multi-Frame Super-Resolution—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Fingerprints of Super Resolution Networks—0
Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging—0
A Unified Plug-and-Play Algorithm with Projected Landweber Operator for Split Convex Feasibility Problems—0
Fingerprinting Deep Image Restoration Models—0
HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation—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
Image Transformer—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network—0
Fine-Grained Neural Architecture Search—0
A statistically constrained internal method for single image super-resolution—0
A Convex Approach for Image Hallucination—0
Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks—0
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers—0
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network—0
Fidelity-Naturalness Evaluation of Single Image Super Resolution—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
FFEINR: Flow Feature-Enhanced Implicit Neural Representation for Spatio-temporal Super-Resolution—0
Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution—0
Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport—0
Higher-order MRFs based image super resolution: why not MAP?—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
High-Frequency aware Perceptual Image Enhancement—0
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS—0
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features—0
AIM 2020 Challenge on Video Temporal Super-Resolution—0
High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network—0
Coupled-Projection Residual Network for MRI Super-Resolution—0
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction—0
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Image Super-Resolution With Deep Variational Autoencoders—0
IMDeception: Grouped Information Distilling Super-Resolution Network—0
Feedback Graph Attention Convolutional Network for Medical Image Enhancement—0
CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified