Deep learning enables contrast|robust super|resolution ...

(PDF) Deep learning enables contrast-robust super-resolution ...

To overcome these challenges, we propose a new method called contrast-robust structured illumination microscopy (CR-SIM). CR-SIM employs a deep ...

Deep learning enables contrast-robust super-resolution ...

Deep learning enables contrast-robust super-resolution reconstruction in structured illumination microscopy. · Yunbo Chen, Qingqing Liu, +6 authors. Wenjie Liu ...

Deep learning enables reference-free isotropic super-resolution for ...

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior ...

Deep learning enables stochastic optical reconstruction microscopy ...

Despite its remarkable potential for transforming low-resolution images, deep learning faces significant challenges in achieving ...

Super-resolution deep learning image reconstruction - NCBI

The recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and ...

Single-frame deep-learning super-resolution microscopy for ... - Nature

In contrast, super-resolution tasks for microscopic images demand ultrastructure recovery from diffraction-limited images with high accuracy.

Fine-tuning deep learning model parameters for improved super ...

Alleviates the spatio-temporal trade-off in dynamic MRI using super-resolution · Model was first trained on a static dataset with different contrast and ...

Deep Learning Super-Resolution Reconstruction for Fast and ...

Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined ...

Mouse brain MR super-resolution using a deep learning network ...

Super-resolution (SR) of MRI data aims to enhance its resolution and diagnostic value. While deep learning-based SR has shown potential, its ...

Super-resolution deep learning reconstruction to improve image ...

Several studies have compared the image quality of SR-DLR with other reconstruction methods in coronary CT angiography, but these studies used ...

Deep learning in computed tomography super resolution using multi ...

As spatial resolution can be defined by the modulation transfer function kernel in CT physics, we propose to train a SR network using paired low ...

Super-Resolution Ultrasound Localization Microscopy Through ...

We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM ...

Super-resolution Ultrasound Localization Microscopy through Deep ...

We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM ...

Deep-learning-based methods for super-resolution fluorescence ...

Deep-learning-based algorithms have achieved state-of-the-art performance in super-resolution fluorescence microscopy and are becoming increasingly attractive.

Super-resolution deep learning reconstruction at coronary computed ...

A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed ...

Deep learning for accelerated and robust MRI reconstruction

This design allows for effective capture of long-distance dependencies in MR images. SwinGAN also features a contextual image relative position ...

SRflow: Deep learning based super-resolution of 4D-flow MRI data

Recently, deep neural networks based on super-resolution (Bhowmik et al., 2018) have become popular due to their high accuracy and fast ...

AI in Microscopy: Deep Learning for Image Analysis - ZEISS

Deep Learning uses a large number of training parameters to capture complex textural details in images. This enables robust image segmentation even when ...

Enhancing image resolution of confocal fluorescence microscopy ...

In this work, we present a deep-learning-based super-resolution technique of confocal microscopy. We devise a two-channel attention network ( ...

Deep Learning Single-Frame and Multiframe Super-Resolution for ...

Convolutional neural networks (CNNs), a form of DL, were trained to perform super resolution in image space by using synthetically generated low ...