Deep Learning-Based Inverse Scattering With Structural Similarity Loss Functions

Deep learning based inverse scattering (DL-IS) methods attract much attention in recent years due to advantages of fast speed and high-quality reconstruction. The loss functions of neural networks in DL-IS methods are commonly based on a pixel-wise mean squared error (MSE) between the reconstructed image and its reference one. In this article, we introduce a structural similarity (SSIM) loss function to combine with the MSE loss for reconstructing dielectric targets under a DL-IS framework. The SSIM loss imposes a further regularization on the target at the perceptual level. Numerical tests for both synthetic and experimental data verify that this new perceptually-inspired loss function can effectively improve the imaging quality and the generalization capability of the trained model.

Automated summary

This article introduces a structural similarity (SSIM) loss function combined with a mean squared error (MSE) loss function for reconstructing dielectric targets in a DL-IS framework. Numerical tests show that this new perceptually-inspired loss function can effectively enhance imaging quality and model generalization capability.