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 1901–1925 of 3874 papers

TitleStatusHype
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Dense U-net for super-resolution with shuffle pooling layer—0
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
J-Net: Improved U-Net for Terahertz Image Super-Resolution—0
Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches—0
Joint convolutional neural pyramid for depth map super-resolution—0
Facial Attribute Capsules for Noise Face Super Resolution—0
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization—0
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation—0
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence—0
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning—0
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement—0
Joint Face Super-Resolution and Deblurring Using a Generative Adversarial Network—0
Joint Flow And Feature Refinement Using Attention For Video Restoration—0
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded—0
Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors—0
Content-adaptive Representation Learning for Fast Image Super-resolution—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Face Super-resolution Guided by Facial Component Heatmaps—0
Constrained Diffusion Implicit Models—0
Face Recognition in Low Quality Images: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified