Advanced Image Processing Techniques

Physics-Driven Zero-Shot Reconstruction of Isotropic 3D Fluorescence Microscopy under Undersampled Acquisition

This paper introduces DeepUI, a physics-driven framework that reconstructs isotropic 3D fluorescence images from undersampled axial data, achieving high performance even under challenging conditions.

Instant Prior-Free Resolution Enhancement for Cross-Modality Microscopy

This paper introduces NFR, a prior-free algorithm that enhances microscopy resolution without requiring PSF estimation, achieving results beyond the Sparrow limit and improving super-resolution techniques.

Evaluating image upsampling strategies for downstream microscopy image classification

This study evaluates how different image upsampling strategies affect classification performance in microscopy images using the BloodMNIST dataset.

Latent-centric Isotropic Resolution Enhancement for Expansion Microscopy Imaging via Neural Compression and Self-supervised Learning

This paper introduces a self-supervised framework for isotropic super-resolution and compression in expansion microscopy, enabling efficient storage and analysis of whole-organ datasets.

High-fidelity bioimage restoration via adversarial learning

This paper introduces NAFNet GAN, a bioimage restoration framework that achieves high fidelity and low latency, suitable for real-time microscopy applications.

MAGNET: an all-in-one foundation model for cross-modal and cross-dimensional microscopic image restoration

MAGNET is a foundation model for microscopic image restoration that handles multiple modalities and tasks, achieving state-of-the-art results across various restoration tasks.

Neural field patient-specific super resolution for enhanced 1.5 Tesla brain MRI visualization

This paper uses neural fields to enhance 1.5 Tesla brain MRI visualization through super-resolution and novel view synthesis, achieving high-quality reconstructions across multiple scaling factors.

Unsupervised deep learning enables blur-free super-resolution in two-photon microscopy

This paper introduces an unsupervised deep learning method for deblurring, super-resolution, and segmentation of two-photon microscopy images, achieving better performance than conventional methods.

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

This paper uses a generative AI model to improve 3D tissue imaging by interpolating and restoring images, enhancing resolution and reducing artifacts across multiple modalities and species.

{Theta}-Net: Achieving Enhanced Phase-Modulated Optical Nanoscopy in silico through a computational 'string of beads' architecture

This paper introduces {Theta}-Net, a computational architecture that improves super-resolved optical microscopy images in silico, outperforming existing frameworks and demonstrating effectiveness under poor signal-to-noise ratios.

Towards robust and generalizable super-resolution generative adversarial networks for magnetic resonance neuroimaging: a cross-population approach

This paper demonstrates that SRGANs can generalize across different populations in MRI without introducing artifacts, showing robustness in real-world applications.

Physics Augmented U-Net: A High-Frequency Aware Generative Prior for Microscopy

The paper introduces a Physics Augmented U-Net model that enhances microscopy resolution by integrating structured illumination microscopy principles with deep learning.