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 2901–2925 of 3874 papers

TitleStatusHype
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras—0
TV-based Deep 3D Self Super-Resolution for fMRI—0
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution—0
Two-dimensional gridless super-resolution method for ISAR imaging—0
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model—0
Two-stage domain adapted training for better generalization in real-world image restoration and super-resolution—0
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images—0
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt—0
UDC: Unified DNAS for Compressible TinyML Models—0
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition—0
UGPNet: Universal Generative Prior for Image Restoration—0
Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction—0
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance—0
Uncertainty-Driven Loss for Single Image Super-Resolution—0
Uncertainty Estimation for Super-Resolution using ESRGAN—0
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model—0
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement—0
Uncertainty Quantification via Neural Posterior Principal Components—0
Understanding Deformable Alignment in Video Super-Resolution—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Underwater litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified