Image inpainting in acoustic microscopy

Abstract

Scanning acoustic microscopy (SAM) is a non-ionizing and label-free imaging modality used to visualize the surface and internal structures of industrial objects and biological specimens. The image of the sample under investigation is created using high-frequency acoustic waves. The frequency of the excitation signals, the signal-to-noise ratio, and the pixel size all play a role in acoustic image resolution. We propose a deep learning-enabled image inpainting for acoustic microscopy in this paper. The method is based on training various generative adversarial networks (GANs) to inpaint holes in the original image and generate a 4× image from it. In this approach, five different types of GAN models are used: AOTGAN, DeepFillv2, Edge-Connect, DMFN, and Hypergraphs image inpainting. The trained model’s performance is assessed by calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) between network-predicted and ground truth images. The Hypergraphs image inpainting model provided an average SSIM of 0.93 for 2× and up to 0.93 for the final 4×, respectively, and a PSNR of 32.33 for 2× and up to 32.20 for the final 4×. The developed SAM and GAN frameworks can be used in a variety of industrial applications, including bio-imaging.