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 3176–3200 of 3874 papers

TitleStatusHype
Space-Time Distillation for Video Super-Resolution—0
Space-Time Video Super-resolution with Neural Operator—0
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography—0
Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction—0
Sparse Based Super Resolution Multilayer Ultrasonic Array Imaging—0
Sparse Coding Approach for Multi-Frame Image Super Resolution—0
Sparse Depth Super Resolution—0
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution—0
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning—0
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Sparsity-Based Super Resolution for SEM Images—0
Video Face Super-Resolution with Motion-Adaptive Feedback Cell—0
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models—0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
An approach to image denoising using manifold approximation without clean images—0
Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI—0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Spatial Frequency Bias in Convolutional Generative Adversarial Networks—0
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach—0
VideoGigaGAN: Towards Detail-rich Video Super-Resolution—0
3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN—0
Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution—0
Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified