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 821–830 of 3874 papers

TitleStatusHype
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-ResolutionCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Learnable Lookup Table for Neural Network QuantizationCode1
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal FusionCode1
High-Resolution Image Editing via Multi-Stage Blended DiffusionCode1
Deep Image PriorCode1
Burst Image Restoration and EnhancementCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion ModelsCode1
Show:102550
← PrevPage 83 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified