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Aim: The primary objective of this study is to evaluate the precision and robustness of the proposed scattering deblurring model in reconstructing three-dimensional complex structures within the scattering medium. Adopting a multidimensional approach, the study integrates deep learning principles to surpass the traditional deblurring method with point-spread function deconvolution, establishing a framework for achieving high-fidelity 3D reconstruction structure combined with the commonly filtered-back projection method. Approach: Leveraging a diverse dataset of simulation images to expose the model to various scattering structures, the proposed scattering deblurring technique is based on the Fully Convolutional Network, Attention Res-Unet. The evaluation of the model’s performance incorporates critical metrics such as the intersection over union (IoU) and the contrast improvement ratio (CIR). Results: The study demonstrates the effectiveness of the proposed scattering-deblurring model in mitigating scattering blur. Evaluation metrics, including a maximum IoU of 0.9737 and a CIR of 7, 166, underscore the superior performance of the proposed method compared to the deconvolution method in the entire angular range. Conclusions: In conclusion, this study underscores the importance of adaptive imaging techniques that address the diverse and complex geometries encountered in biomedical optics. The proposed scattering-deblurring model, anchored in deep learning principles, presents promising results in enhancing the visualization of light-absorbing structures within turbid media. |
