Advanced Image Processing Techniques

From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging

This paper introduces a device-agnostic deep learning framework for super-resolution imaging that eliminates calibration needs and achieves high accuracy across diverse imaging setups.

niiv: Interactive Self-supervised Neural Implicit Isotropic Volume Reconstruction

The paper introduces niiv, a self-supervised method for isotropic reconstruction of 3D microscopy data, achieving faster and higher-quality results than existing methods.

RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using Autoencoders

RAVEN upsamples brain MRIs to higher resolution, preserving anatomical details across various modalities and field strengths.

Time-lapse Image Super-resolution Neural Network with Reliable Confidence Quantification for Optical Microscopy

This paper introduces a time-lapse image super-resolution neural network with confidence quantification for optical microscopy, improving temporal consistency and reliability.

PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy

PSSR2 provides a user-friendly Python package for applying deep learning-based super-resolution and denoising to undersampled microscopy data without requiring programming expertise.

Clinical super-resolution computed tomography of bone microstructure: application in musculoskeletal and dental imaging

This paper develops a deep learning-based super-resolution method to enhance clinical CT image resolution for musculoskeletal and dental imaging, showing improved microstructural detail but with some artifacts and grayscale distribution changes.

Resolution Enhancement with a Task-Assisted GAN to Guide Optical Nanoscopy Image Analysis and Acquisition

This paper introduces TA-GAN, a deep learning model that enhances image resolution in optical nanoscopy by incorporating auxiliary tasks, improving accuracy and enabling online imaging assistance.

Small Training Dataset Convolutional Neural Networks for Application Specific Super-Resolution Microscopy

This paper introduces DenseED blocks to train super-resolution microscopy networks with small datasets, achieving higher PSNR and resolution than conventional models.

Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain

This paper uses an autoencoder to enhance through-plane resolution in fetal brain diffusion MRI, outperforming interpolation methods on pre-term newborn data.

Deep Learning-Based Point-Scanning Super-Resolution Imaging

This paper introduces a deep learning method to enhance the resolution and speed of point-scanning imaging systems by restoring undersampled images.

A 3D High Resolution Generative Deep-learning Network for Fluorescence Microscopy Image

This paper introduces a 3D deep learning network using dual-GAN to recover high-resolution volume images from low-resolution ones in fluorescence microscopy.

Fourier ring correlation simplifies image restoration in fluorescence microscopy

This paper introduces blind image restoration methods using Fourier ring correlation (FRC) for de-noising and deconvolution in fluorescence microscopy, showing improved performance in 2D and 3D images.

Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy

This paper demonstrates how deep learning-based image restoration extends fluorescence microscopy's capabilities by improving resolution, reducing photon exposure, and increasing frame rates.