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

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Showing 2101–2125 of 3874 papers

TitleStatusHype
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization—0
StyleDEM: a Versatile Model for Authoring Terrains—0
Quantum Annealing for Single Image Super-Resolution—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
Learning-based Framework for US Signals Super-resolution—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
Arbitrary Reduction of MRI Inter-slice Spacing Using Hierarchical Feature Conditional Diffusion—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution—0
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution—0
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
Super-Resolving Face Image by Facial Parsing InformationCode0
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging in vivo—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Image-to-image domain adaptation for vehicle re-identification—0
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-ResolutionCode0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
Random Weights Networks Work as Loss Prior Constraint for Image Restoration—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-ResolutionCode0
Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified