Study of Image Classification Accuracy with Fourier Ptychography


In this research, the accuracy of image classification with Fourier Ptychography Microscopy (FPM) has been systematically investigated. Multiple linear regression shows a strong linear relationship between the results of image classification accuracy and image visual appearance quality based on PSNR and SSIM with multiple training datasets including MINST, Fashion MNIST, Cifar, Caltech 101, and customized training datasets. It is, therefore, feasible to predict the image classification accuracy only based on PSNR and SSIM. It is also found that the image classification accuracy of FPM reconstructed with higher resolution images is significantly different from using the lower resolution images under the lower numerical aperture (NA) condition. The difference is yet less pronounced under the higher NA condition.



fourier ptychography, image classification, deep learning, neural network


Zhang, H.; Zhang, Y.; Wang, L.; Hu, Z.; Zhou, W.; Tsang, P.W.M.; Cao, D.; Poon, T.-C. Study of Image Classification Accuracy with Fourier Ptychography. Appl. Sci. 2021, 11, 4500.