E-Unet: a deep learning method for photoacoustic signal enhancement

Abstract

We developed a deep learning algorithm, called enhancement Unet (E-Unet), to improve the signal-to-noise ratio (SNR) of signals acquired in a photoacoustic computed microscopy (PAM) system. We tried various combination of custom loss functions which included peak-amplitude, peak-position and mean-squared signal value with Adam optimizer for training purposes. For the testing purposes, we acquired PAM data with complicated phantoms in biological tissue. The performance of the improved signals is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and Pearson correlation

Signal denoising for thermal acoustic imaging

“…These denoising techniques include signal processing tools, machine and deep learning tools and some other innovative algorithms. Deep learning has the potential to learn complex relationships and excel in extracting meaning information [1][2][3][4][5][6][7][8][9][10]. In paper [11], the author proposes the application of Convolutional Neural Network Denoising Auto-Encoders to intelligently filter noise in aircraft engine gas path health signals, enhancing signal quality for improved diagnostic accuracy.…”

“…Recently, in paper [15], the authors used generative adversarial network to enhance the structural signal to assess the structural damage after disasters such as earthquake. In previous works [16][17][18], the authors extract the meaningful information and understand the relationships within signal using a modification of convolutional layers network.…”