Neural network for enhancing microscopic resolution based on images from scanning electron microscope

Chia Hung Dylan Tsai*, Chia Hao Yeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper, an artificial neural network is applied for enhancing the resolution of images from an optical microscope based on a network trained with the images acquired from a scanning electron microscope. The resolution of microscopic images is important in various fields, especially for microfluidics because the measurements, such as the dimension of channels and cells, largely rely on visual information. The proposed method is experimentally validated with microfluidic structure. The images of structural edges from the optical microscope are blurred due to optical effects while the images from the scanning electron microscope are sharp and clear. Intensity pro-files perpendicular to the edges and the corresponding edge positions determined by the scanning electron microscope images are plugged in a neural network as the input features and the output target, respectively. According to the results, the blurry edges of the microstructure in optical images can be successfully enhanced. The average error between the predicted channel position and ground truth is around 328 nanometers. The effects of the feature length are discussed. The proposed method is expected to significantly contribute to microfluidic applications, such as on-chip cell evaluation.

Original languageEnglish
Article number2139
Pages (from-to)1-18
Number of pages18
Issue number6
StatePublished - 2 Mar 2021


  • Artificial neural network
  • Image enhancement
  • Microfluidics
  • Microscope


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