Imaging targets hidden in scattering and viscous liquid-based media by combining multiple projections and applying a non-local mean filtering algorithm

Abstract

Scattering and viscous (or sticky) liquid-based media such as that in the human ear can be considered a highly turbid environment through which imaging a target is challenging. Typically, reduced target visibility and image quality together with the limitation of depth through which objects may be resolved occurs because of multiple scattering events. Consequently, several approaches described in the literature have been applied to image objects hidden within turbid liquid media. In this paper, we experimentally demonstrate an approach to enhance image quality during recovery of objects hidden in sticky liquid at different viscosity levels by the fusion of single-shot projections assisted by a non-local mean (NLM) filtering algorithm. The turbidity of the medium was evaluated via attenuation coefficient by Beer's law and the viscosity was measured by a capillary viscometer device. The medium was illuminated with a laser beam and multiple images of the object were obtained from different perspectives using a lens array. Following image processing, each sub-image (projection) was digitally cropped, extracted from the array, filtered, shifted to a common center and then summed with other sub-images to form a single average image. Different setup conditions such as: medium turbidity, target shape, illumination state, and filtering processing were tested and compered during this work. A quantitative image quality metric of visibility commonly used in image processing was adopted to evaluate the quality of the reconstructed targets. The feasibility of the proposed hybrid method is supported by the experimental results which show a reconstructed image with high visibility. This study is a part of our ongoing efforts to enable imaging and reveal objects embedded in turbid media by integrating different strategies from the disciplines of: optics, astrophysics, digital image processing, and medicine.