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 1701–1710 of 3874 papers

TitleStatusHype
Rethinking Implicit Neural Representations for Vision Learners—0
AERO: Audio Super Resolution in the Spectral DomainCode2
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time—0
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution—0
Blur Interpolation Transformer for Real-World Motion from BlurCode2
N-Gram in Swin Transformers for Efficient Lightweight Image Super-ResolutionCode1
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution—0
Guided Depth Super-Resolution by Deep Anisotropic DiffusionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified