Scattering imaging as a noise removal in digital holography by using deep learning. (1st August 2022)

• Record Type: Journal Article
• Authors: Liao, Meihua; Feng, Yuliu; Lu, Dajiang; Li, Xianye; Pedrini, Giancarlo; Frenner, Karsten; Osten, Wolfgang; Peng, Xiang; He, Wenqi
• Is Part Of: New journal of physics. Volume 24:Number 8(2022)
• Publication Date: 2022-08-01
• Subjects: scattering imaging -- digital holography -- deep learning; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/ac8308
• Languages: English
• ISSNs: 1367-2630

Abstract

Abstract: Imaging through scattering media is one of the main challenges in optics while the deep learning (DL) technique is well known as one of the promising ways to handle it. However, most of the existing DL approaches for imaging through scattering media adopt the end-to-end strategy, which significantly limits its generalization capability for various or dynamic scattering media. In this work, we propose an alternative DL-based method to achieve the goal of imaging through different scattering media under the framework of off-axis digital holography. As a result, the severe ill-posed inverse problem in scattering imaging is simplified as a relatively easy denoising issue for a deteriorated hologram. The experimental results of the proposed method show good generalization for not only different scattering media but also different types of objects.