Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

• Main Authors: Md. Shahinur Alam, Ki-Chul Kwon, Munkh-Uchral Erdenebat, Mohammed Y. Abbass, Md. Ashraful Alam, Nam Kim
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Sensors
• Subjects: deep learning; generative adversarial network; integral imaging microscopy; machine intelligence; microscopy
• Online Access: https://www.mdpi.com/1424-8220/21/6/2164

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.