Breaking tunnel vision, imaging AI lifts fluorescence image restoration accuracy and speed

Recent years have witnessed great advances in applying deep learning to improve fluorescence microscopy imaging. However, enhancing the fidelity of image restoration networks and improving their ...
GitHub

DAM-VSR: Disentanglement of Appearance and Motion for Video Super-Resolution (ACM SIGGRAPH 2025)

conda create -n dam-vsr python=3.10.14 conda activate dam-vsr pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org ...
IEEE

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI

Abstract: Objective: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method & Materials: Previous CNN-based MRI super ...
IEEE

Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution

Abstract: This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a ...
marktechpost

Researchers From INRIA France Propose ‘Pythae’: An Open-Source Python Library Unifying Common And State-of-the-Art Generative AutoEncoder (GAE) Implementations

Variational AutoEncoders (VAE) have grown in popularity due to their scalability and computational efficiency. It is widely used in voice modeling, clustering, and data augmentation applications. This ...
Frontiers

Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules

Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the ...