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 2926–2950 of 3874 papers

TitleStatusHype
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns—0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
Unpaired MRI Super Resolution with Contrastive Learning—0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression—0
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Unsupervised Learning for Real-World Super-Resolution—0
Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses—0
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective—0
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation—0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples—0
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified