Core Concepts
Strategic compromises in biophotonic imaging metrics can be effectively counterbalanced by specialized deep learning models, enhancing various imaging parameters crucial for advanced applications.
Abstract
This article explores the integration of deep learning with biophotonic setups to compensate for compromised measurement metrics. It covers refocusing methods, reconstruction with less data, and improving image quality and throughput. Various techniques leveraging deep learning are discussed, showcasing the transformative potential of AI in bioimaging.
1 Introduction
Integration of deep learning with biophotonics heralds an era of transformative enhancements.
Neural network compensation involves strategic compromises for later restoration through AI.
Various imaging systems involve deliberate impairments compensated by deep learning models.
2 Refocusing
Traditional vs. computational refocusing methods using neural networks.
Deep-R and W-Net models accelerate autofocusing and enhance DOF through PSF engineering.
GANscan method restores sharpness in motion-blurred scans efficiently.
3 Reconstruction with Less Data
Purposeful undersampling combined with neural network compensation enhances image reconstruction.
Fourier ptychography, volumetric microscopy, SS-OCT leverage deep learning to improve imaging speed and reduce photodamage.
4 Improving Image Quality and Throughput
Deep learning corrects optical distortions in mobile-phone microscopy for high-quality images.
STED microscopy benefits from low exposure times using UNet-RCAN model.
SRS microscopy denoising via U-Net improves SNR significantly.
5 Discussion and Future Perspectives
Combining different defects strategically could further enhance imaging processes.
Regulatory challenges exist when compromising data integrity for faster imaging processes.
Deep learning's reliability can be established through rigorous validation protocols.