High-resolution imaging in acoustic microscopy using deep learning
Pragyan Banerjee, SHIVAM MILIND AKARTE, Prakhar Kumar, Muhammad Shamsuzzaman, Ankit Butola, Krishna Agarwal, Dilip K. Prasad, Frank Melandsø, Anowarul Habib
Abstract
Abstract Acoustic microscopy is a cutting-edge label-free imaging technology that allows us to see the surface and interior structure of industrial and biological materials. The acoustic image is created by focusing high-frequency acoustic waves on the object and then detecting reflected signals. On the other hand, the quality of the acoustic image’s resolution is influenced by the signal-to-noise ratio, the scanning step size, and the frequency of the transducer. Deep learning-based high-resolution imaging in acoustic microscopy is proposed in this paper. To illustrate four times resolution improvement in acoustic images, five distinct models are used: SRGAN, ESRGAN, IMDN, DBPN-RES-MR64-3, and SwinIR. The trained model’s performance is assessed by calculating the PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) between the network-predicted and ground truth images. To avoid the model from over-fitting, transfer learning was incorporated during the procedure. SwinIR had average SSIM and PSNR values of 0.95 and 35, respectively. The model was also evaluated using a biological sample from Reindeer Antler, yielding an SSIM score of 0.88 and a PSNR score of 32.93. Our framework is relevant to a wide range of industrial applications, including electronic production, material micro-structure analysis, and other biological applications in general.