FocusNET: An autofocusing learning‐based model for digital lensless holographic microscopy

ABSTRACT: This paper reports on a convolutional neural network (CNN) – based regression model, called FocusNET, to predict the accurate reconstruction distance of raw holograms in Digital Lensless Holographic Microscopy (DLHM). This proposal provides a physical-mathematical formulation to extend its use to different DLHM setups than the optical and geometrical conditions utilized for recording the training dataset; this unique feature is tested by applying the proposal to holograms of diverse samples recorded with different DLHM setups. Additionally, a comparison between FocusNET and conventional autofocusing methods in terms of processing times and accuracy is provided. Although the proposed method predicts reconstruction distances with approximately 54 µm standard deviation, accurate information about the samples in the validation dataset is still retrieved. When compared to a method that utilizes a stack of reconstructions to find the best focal plane, FocusNET performs 600 times faster, as no hologram reconstruction is needed. When implemented in batches, the network can achieve up to a 1200-fold reduction in processing time, depending on the number of holograms to be processed. The training and validation datasets, and the code implementations, are hosted on a public GitHub repository that can be freely accessed.

• Autores: Pabón Vidal, Adriana Lucía; García Sucerquia, Jorge Iván; Gómez Ramírez, Alejandra; Herrera Ramírez, Jorge Alexis; Buitrago Duque, Carlos Andrés; Lopera Acosta, María Josef; Montoya, Manuel; Trujillo Anaya, Carlos Alejandro
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2023
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/42049
• Acceso en línea: https://hdl.handle.net/10495/42049
• Palabra clave: Aprendizaje Profundo; Deep Learning; Microscopía; Microscopy; https://id.nlm.nih.gov/mesh/D000077321; https://id.nlm.nih.gov/mesh/D008853
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/2.5/co/