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 2976–3000 of 3874 papers

TitleStatusHype
Understanding Deformable Alignment in Video Super-Resolution—0
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labelsCode0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results—0
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network—0
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems—0
Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission—0
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Interpretable Deep Multimodal Image Super-Resolution—0
Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation—0
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS—0
Image Super-Resolution using Explicit Perceptual Loss—0
Accelerated WGAN update strategy with loss change rate balancing—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Cascade Convolutional Neural Network for Image Super-Resolution—0
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—0
A Study of Efficient Light Field Subsampling and Reconstruction Strategies—0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems—0
Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images—0
Deep Photo Cropper and Enhancer—0
Binarized Neural Network for Single Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified