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 226–250 of 3874 papers

TitleStatusHype
Deep Audio Waveform PriorCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC AlgorithmCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
Deep Blind Super-Resolution for Satellite VideoCode1
DeFMO: Deblurring and Shape Recovery of Fast Moving ObjectsCode1
Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-ResolutionCode1
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark EstimationCode1
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Deep Learning-Driven Ultra-High-Definition Image Restoration: A SurveyCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified