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 3101–3150 of 3874 papers

TitleStatusHype
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
MRI Super-Resolution with GAN and 3D Multi-Level DenseNet: Smaller, Faster, and Better—0
Always Look on the Bright Side of the Field: Merging Pose and Contextual Data to Estimate Orientation of Soccer Players—0
Residual learning based densely connected deep dilated network for joint deblocking and super resolution—0
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning—0
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data—0
RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling Based Video Coding—0
3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution—0
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-ResolutionCode0
Generator From Edges: Reconstruction of Facial Images—0
Facial Attribute Capsules for Noise Face Super Resolution—0
Video Face Super-Resolution with Motion-Adaptive Feedback Cell—0
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution—0
Reconstructing the Noise Manifold for Image Denoising—0
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
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
A Generative Adversarial Network for AI-Aided Chair Design—0
Learning Deep Analysis Dictionaries for Image Super-Resolution—0
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
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
Neural Architecture Search for Deep Image PriorCode0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Learned Multi-View Texture Super-Resolution—0
Segmentation and Generation of Magnetic Resonance Images by Deep Neural NetworksCode0
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation—0
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
Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE LayerCode0
VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video ProcessingCode0
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES—0
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
Rapid Whole-Heart CMR with Single Volume Super-resolution—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—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
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
Adaptive Densely Connected Super-Resolution ReconstructionCode0
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified