Over the past several years, ghost imaging has made remarkable achievements in poor optical conditions, including underwater imaging works. This study describes the interactions between environmental interference and experimental results of ghost imaging applied to underwater scenarios deduced by mathematical processes and related researches findings. In this paper, the causes of notable optical influences: absorption, scattering, and turbulence are firstly presented in the form of statistical mathematics; Sequentially, at the level of complex amplitude in wave optics, the experimental principle: second-order correlation are calculated as a basic for subsequent discussion in physical illustration; further, mainly from existing researches, the text expounds the specific influence of environmental and experimental factors on the imaging quality with above-mentioned models.
Ghost imaging (GI) possesses significant application prospects in scattering imaging, which is a classic example of underdetermined conversion problem in optical field. However, even under the framework of single-pixel imaging (SPI), a challenge remains unresolved, i.e., structured patterns may be damaged by scattering media in both the emissive and receiving optical paths. In this study, an extendible ghost imaging, a numerical reproduction of the qualitative process using deep learning (DL)-based GI is presented. First, we propose and experimentally verify a brief degradation-guided reconstruction (DR) approach with a neural network to demonstrate the degradation principle of scattering, including realistic dataset simulations and a new training structure in the form of a convolutional neural network (CNN). Then, a novel photon contribution model (PCM) with redundant parameters is proposed to generate intensity sequences from the forward direction through volumetric scattering media; the redundant parameters are constructed and relate to the special output configuration in a lightweight CNN with two branches, based on a reformulated atmospheric scattering model. The proposed scheme recovers the semantics of targets and suppresses the imaging noise in the strong scattering medium, and the obtained results are very satisfactory for applications to scattering media of more practical scenarios and are available for various scattering coefficients and work distances of an imaging prototype. After using DL methods in computational imaging, we conclude that strategies embedded in optics or broader physical factors can result in solutions with better effects for unanalyzable processes.
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