Computational ghost imaging using the dilated ghost network

Computational ghost imaging has poor imaging quality at low sampling rates. To solve this issue, a dilated ghost network is proposed to improve the quality of noisy images reconstructed by computational ghost imaging in this paper. The network builds upon the traditional GhostNet framework, which minimizes computational costs while maintaining stable performance. By incorporating dilated convolutions, the network expands the receptive field without adding additional parameters. However, the use of dilated convolutions can cause a gridding problem, which degrades the imaging quality. To solve this problem, a sawtooth wave-like structure is introduced into the network. Simulation experiments, combined with metrics such as SSIM, PSNR, and RMSE, validate both the existence of the gridding problem and the effectiveness of the sawtooth wave-like structure in overcoming it. The experimental results indicate that the proposed method significantly outperforms traditional and compressive-sensing-based CGI methods. © 2024 Elsevier B.V.