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 3776–3800 of 3874 papers

TitleStatusHype
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications—0
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution—0
Some medical applications of example-based super-resolution—0
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution—0
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution—0
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip ConnectionsCode0
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos—0
Perceptual Losses for Real-Time Style Transfer and Super-ResolutionCode1
Global-Local Face Upsampling Network—0
Image Super-Resolution Based on Sparsity Prior via Smoothed l_0 Norm—0
Con-Patch: When a Patch Meets its Context—0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
Structured illumination microscopy image reconstruction algorithmCode0
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization—0
Studying Very Low Resolution Recognition Using Deep Networks—0
Matrix Neural Networks—0
Matrix Variate RBM and Its Applications—0
Image Resolution Enhancement by Using Interpolation Followed by Iterative Back Projection—0
Face Hallucination using Linear Models of Coupled Sparse Support—0
Double Sparse Multi-Frame Image Super Resolution—0
Example-Based Modeling of Facial Texture From Deficient Data—0
Hyperspectral Super-Resolution by Coupled Spectral Unmixing—0
Convolutional Sparse Coding for Image Super-Resolution—0
Naive Bayes Super-Resolution Forest—0
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified