Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN

Abstract

The finite-difference (FD) method is one of the most commonly used numerical methods for elastic wave modeling. However, due to the difference approximation of the derivative, the time dispersion phenomenon cannot be avoided. This paper proposes the use of pix2pix algorithm based on a conditional generative adversarial network (cGAN) for removing time dispersion from elastic FD modeling. Firstly, we analyze the time dispersion of elastic wave FD modeling. Then, we discuss the pix2pix algorithm based on cGAN, improve the loss function of the pix2pix algorithm by introducing a Sobel operator, and analyze the parameter selection of the network model for the pix2pix algorithm. Finally, we verify the feasibility and effectiveness of the pix2pix algorithm in removing time dispersion from elastic wave FD modeling through testing some model simulation data.

Deep Learning Realizes Photoacoustic Imaging Artifact Removal

“…Then, the Pix2Pix algorithm (Figure 3b) is employed to process the artifacts in the photoacoustic image caused by radiation artifacts and the mixing of the photoacoustic source, enabling comprehensive artifact removal in the photoacoustic image. Pix2Pix [35,36] is a Conditional Generative Adversarial Network (cGAN)-based image-to-image translation model designed for image transformation tasks that require explicit one-to-one correspondence, and can effectively remove aliasing artifacts within optical sound sources. The model consists of two core components, the generator (G) and the discriminator (D).…”

“…YOLOv8 is a target detection algorithm for automatic identification and removal of artifacts in photoacoustic images, with efficient real-time target detection and good Pix2Pix [35,36] is a Conditional Generative Adversarial Network (cGAN)-based imageto-image translation model designed for image transformation tasks that require explicit one-to-one correspondence, and can effectively remove aliasing artifacts within optical sound sources. The model consists of two core components, the generator (G) and the discriminator (D).…”