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 931–940 of 3874 papers

TitleStatusHype
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-ResolutionCode1
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-ResolutionCode1
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving ObjectsCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolutionCode1
Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion ModelCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
Show:102550
← PrevPage 94 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified