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 2901–2950 of 3874 papers

TitleStatusHype
Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution ImagingCode0
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis—0
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias ClassificationCode0
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution—0
Model Adaptation for Inverse Problems in Imaging—0
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images—0
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images—0
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution—0
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change—0
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning—0
On-Device Text Image Super Resolution—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—0
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution—0
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-ResolutionCode0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Dense U-net for super-resolution with shuffle pooling layer—0
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets—0
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging—0
Joint Super-Resolution and Rectification for Solar Cell Inspection—0
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution—0
EPSR: Edge Profile Super resolution—0
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain—0
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution—0
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
Super-Resolution of Real-World Faces—0
Physics-Informed Neural Network Super Resolution for Advection-Diffusion Models—0
Generating Unobserved Alternatives—0
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference—0
Wavelet Flow: Fast Training of High Resolution Normalizing FlowsCode0
Satellite Image Small Target Application Based on Deep Segmented Residual Neural Network—0
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples—0
Multi-Frame Super-Resolution Combining Demons Registration and Regularized Bayesian Reconstruction—0
Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network—0
Super-Resolution Reconstruction of Interval Energy Data—0
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face RecognitionCode0
Super-resolution of periodic signals from short sequences of samples—0
Non-convex Super-resolution of OCT images via sparse representation—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset—0
Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation—0
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified