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 61–70 of 3874 papers

TitleStatusHype
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics—0
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface—0
Label-free Super-Resolution Microvessel Color Flow Imaging with Ultrasound—0
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding—0
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
Deep Spectral Prior—0
Memory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy—0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment—0
SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting—0
Show:102550
← PrevPage 7 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified