Detection or diagnostic sensitivity is not high (usually less than 90% accuracy), and 3D monitoring of samples in a large volume is especially limited because BF images only provide two-dimensional (2D) amplitude information within the shallow depth of focus (DOF).ĭigital holographic microscopy (DHM) is a powerful imaging technique that encrypts the 3D information of a test sample into a single shot of 2D interference patterns (i.e., hologram). BF images have been commonly used to observe microscale objects, such as emulsions, microorganisms, and biological samples 1, 2, 3, 4, 5. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.Ĭonventional bright-field (BF) microscopy using a white light source is a widespread imaging technique used in various areas, including industrial, biological, and medical fields. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3–5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information.
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