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 491–500 of 3874 papers

TitleStatusHype
Advanced Underwater Image Quality Enhancement via Hybrid Super-Resolution Convolutional Neural Networks and Multi-Scale Retinex-Based Defogging Techniques—0
MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks—0
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution—0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
Super-resolving Real-world Image Illumination Enhancement: A New Dataset and A Conditional Diffusion ModelCode0
Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches—0
Spatio-Temporal Distortion Aware Omnidirectional Video Super-ResolutionCode0
Degradation Oriented and Regularized Network for Blind Depth Super-ResolutionCode1
Optimizing Fingerprint-Spectrum-Based Synchronization in Integrated Sensing and Communications—0
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI SegmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified