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 3101–3125 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified