• Image Processing Softwares


• FairSIM
• SIM Check


• Super-Resolution


• Papers that referenced Luhong Jin paper


• [1] Answering some questions about structured illumination microscopy [pdf]
• [2] A survey on applications of deep learning in microscopy image analysis [pdf]
• [3] Deep Learning in Biomedical Optics [pdf]
• [4] Numerical dark-field imaging using deep-learning [pdf]
• [5] High Throughput, Label Free, and Slide Free Histological Imaging by Computational Microscopy and Unsupervised Learning [pdf]
• [6] Review of 4Pi Fluorescence Nanoscopy [pdf]
• [7] Recent Progress in the Correlative Structured Illumination Microscopy [pdf]
• [21] Improving axial resolution in Structured Illumination Microscopy using deep learning [pdf]
• [38] ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning [pdf]

Nuts and Bolts of Deep learning assisted Structural Illumination Microscopy



• Terminologies


• Machine Learning: When a computer acquires the ability to solve a task through examples, rather than human-encoded of handcrafted rules. (Deep Learning with PyTorch, Eli stevens)
• Transfer Learning


• Deep learning: a technique that leverages large amounts of data to approximate complex functions whose inputs and outputs are far apart. (Deep Learning with PyTorch, Eli stevens)


• Aritifical Neural Network: a sequence of layers that each perform simple operation [38]


• Convolutional layer: utilizing spatial information by applying fitlers to patches of neighbouhood pixels [38]
• Residual Neural Network: a network that links past layers to present layers by skip connections to avoid the vanishing gradient problem [38]


• Past and Current Members Contribution


• Ryan
• Bereket

(C) 2022