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 476–500 of 3874 papers

TitleStatusHype
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale ApproachCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Human Pose Estimation on Privacy-Preserving Low-Resolution Depth ImagesCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter EstimationCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
HyperTransformer: A Textural and Spectral Feature Fusion Transformer for PansharpeningCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Image Restoration Through Generalized Ornstein-Uhlenbeck BridgeCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Blueprint Separable Residual Network for Efficient Image Super-ResolutionCode1
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
Image super-resolution via dynamic networkCode1
Image Super-resolution with An Enhanced Group Convolutional Neural NetworkCode1
Deep Blind Super-Resolution for Satellite VideoCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified