@techreport{oai:ipsj.ixsq.nii.ac.jp:00241340,
 author = {Thanawat, Tangpornpisit and Lilies, Handayani and Denis, Chegodaev and Rik, Gijs Gerrit Raes and Kenji, Satou and Thanawat, Tangpornpisit and Lilies, Handayani and Denis, Chegodaev and Rik, Gijs Gerrit Raes and Kenji, Satou},
 issue = {12},
 month = {Nov},
 note = {The application of deep learning to the microscopic Extended Depth of Field (EDoF) images generation is on the rise as an alternative to the traditional time-consuming method. In this research, the packing mechanism from the self-driving car field is incorporated into the cytology images problem. Additionally, color transfer algorithm was selected as a post-processing method. We proposed novel models for both grayscale and RGB images. The evaluation of the model is then compared with the state-of-the-art and significant improvement was discovered with the metrics of Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index (SSIM)., The application of deep learning to the microscopic Extended Depth of Field (EDoF) images generation is on the rise as an alternative to the traditional time-consuming method. In this research, the packing mechanism from the self-driving car field is incorporated into the cytology images problem. Additionally, color transfer algorithm was selected as a post-processing method. We proposed novel models for both grayscale and RGB images. The evaluation of the model is then compared with the state-of-the-art and significant improvement was discovered with the metrics of Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index (SSIM).},
 title = {Convolutional neural network with packing mechanism and color transfer post processing for extended depth of field cervical cytology images},
 year = {2024}
}