Optimized reconstruction processing of targets hidden in turbid environment from multi-perspective images assisted with sorting algorithm-based quality metrics

In the work described in this paper, we experimentally demonstrate an efficient method to improve reconstruction of objects embedded in turbid media. The method combines multiple polarized speckle projections and a fully automated sorting algorithm. In the experimental setup, the medium was illuminated with a polarized laser beam and multiple polarized images of the object were obtained from different viewing perspectives using a lens array and captured by a camera. In offline image processing, each sub-image was digitally cropped, extracted from the array, and qualitatively evaluated by contrast-to-noise ratio (CNR) and entropy metrics. Sub-images were sorted based on quality as determined by CNR and entropy values, with high CNR and low entropy considered as highest quality with the best resolution and contrast. The images were then shifted to a common center and then summed with other sub-images to form a single average image. Experiments to demonstrate the effectiveness of the proposed algorithm were carried out on targets with different geometrical shapes embedded within scattering media in transmission configuration. Analysis of image metrics indicated improvement in object reconstruction by using circular polarization as compared to the use of linear polarization or non-polarization states. The overall experimental results obtained in this work illustrate effective performance of the proposed method.