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 3051–3100 of 3874 papers

TitleStatusHype
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-ResolutionCode0
SynFi: Automatic Synthetic Fingerprint GenerationCode1
Facial Attribute Capsules for Noise Face Super Resolution—0
Generator From Edges: Reconstruction of Facial Images—0
Video Face Super-Resolution with Motion-Adaptive Feedback Cell—0
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite ImageryCode1
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution—0
EndoL2H: Deep Super-Resolution for Capsule EndoscopyCode1
Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive ConvolutionCode1
Reconstructing the Noise Manifold for Image Denoising—0
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual PerceptionCode1
Multiple Angles of Arrival Estimation using Neural Networks—0
Super-resolution of multispectral satellite images using convolutional neural networks—0
Large Hole Image Inpainting With Compress-Decompression Network—0
A Generative Adversarial Network for AI-Aided Chair Design—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
Sound field reconstruction in rooms: inpainting meets super-resolutionCode1
Optimizing Generative Adversarial Networks for Image Super Resolution via Latent Space Regularization—0
Computational resolution limit: a theory towards super-resolution—0
Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques—0
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial NetworkCode1
Multimodal Deep Unfolding for Guided Image Super-Resolution—0
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography—0
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Learned Multi-View Texture Super-Resolution—0
Neural Architecture Search for Deep Image PriorCode0
Segmentation and Generation of Magnetic Resonance Images by Deep Neural NetworksCode0
Fast Adaptation to Super-Resolution Networks via Meta-LearningCode1
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation—0
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization—0
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation—0
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE LayerCode0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
HighRes-net: Multi-Frame Super-Resolution by Recursive FusionCode1
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database—0
Characteristic Regularisation for Super-Resolving Face Images—0
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks—0
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution—0
CNN-generated images are surprisingly easy to spot... for nowCode1
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Exploiting Style and Attention in Real-World Super-Resolution—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified