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 3426–3450 of 3874 papers

TitleStatusHype
Masked Autoencoders are PDE LearnersCode0
Single image super-resolution based on trainable feature matching attention networkCode0
MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-ResolutionCode0
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolutionCode0
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-ResolutionCode0
MTVNet: Mapping using Transformers for Volumes -- Network for Super-Resolution with Long-Range InteractionsCode0
Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning PriorCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Universally Slimmable Networks and Improved Training TechniquesCode0
Maintaining Natural Image Statistics with the Contextual LossCode0
Accurate Image Super-Resolution Using Very Deep Convolutional NetworksCode0
VibrantLeaves: A principled parametric image generator for training deep restoration modelsCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus ImagingCode0
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
A Deep Journey into Super-resolution: A surveyCode0
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approachCode0
Deep Fourier Up-SamplingCode0
Super-Resolution Reconstruction from Bayer-Pattern Spike StreamsCode0
Patch-based Progressive 3D Point Set UpsamplingCode0
Patch-Based Stochastic Attention for Image EditingCode0
Patch-Ordering as a Regularization for Inverse Problems in Image ProcessingCode0
MAANet: Multi-view Aware Attention Networks for Image Super-ResolutionCode0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified