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 701–710 of 3874 papers

TitleStatusHype
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
Fast Neural Representations for Direct Volume RenderingCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-ResolutionCode1
Revisiting Temporal Alignment for Video RestorationCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Rethinking the modeling of the instrumental response of telescopes with a differentiable optical modelCode1
Show:102550
← PrevPage 71 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified