## 5 Conclusions  
This research addressed the challenging tasks of de-blurring caused by scattering, restoring complex absorbing structures, and estimating the depths of complex structures presented in the transilluminated image of biological tissue. The key contribution of our work lies in developing a pixel-to-pixel scanning method that incorporates deep learning models to provide information and depth values for a given blurred image of absorption structures with multiple depth levels. This novel approach enables us to associate various depths with each pixel and then estimate the depths of the absorbing structures in the whole image. Additionally, it should be noted that when a full viewing angle is available,  
Figure 19: Evaluating the feasibility of scatter de-blurring using the CNN model: (**a**) from the observed image with clear medium, (**b**) from the observed image with scattering medium, (**c**) result using the deconvoluted technique, (**d**) result using the FCN model, and (**e**) result using the CNN model with \(\text{threshold}_{\text{depth}}\geq 0.1\). Figure 18: Three-dimensional image reconstructed using the proposed technique from a single blurred image with depth color scale (mm) with (**a**) the scattering de-blurring process and (**b**) result of the 3D reconstruction. this study has demonstrated the ability to reconstruct complete 3D structures, providing a comprehensive understanding of the structures within the imaged medium. Integrating the U-Net and CNN models in the reconstruction process has yielded remarkable results. Combining the clear and de-blurred 2D image from the U-Net model with multiple depth estimations from the CNN model, we obtain a comprehensive 3D representation of the absorbing structures within the turbid medium. This multidimensional insight provides valuable information for researchers and experts and enables our understanding of the complex nature of absorption structures within turbid media and other related domains. Although this approach leverages the capabilities of deep learning models, it is essential to acknowledge the challenges related to data size and computational power. Creating large datasets for training and conducting computationally intensive operations requires careful consideration and optimization. The expanding capability of scattering suppression and depth estimation for absorption structures in turbid mediums using deep learning, combined with our pixel-to-pixel scanning method, represents a significant achievement. This technique can be applied to the advancement of medical imaging and other related fields. Using the strengths of the U-Net and CNN models and the novel depth estimation process, researchers gain access to a powerful tool for reconstructing 3D structures from 2D images or even a single 2D image. Conceptualization, T.N.T. and K.S. ; methodology, T.N.T. ; software, H.N.H. ; validation, N.A.D.N. ; analysis, N.A.D.N. ; investigation, T.N.T. and H.N.H. ; resources, T.N.T. ; data curation, N.A.D.N. and H.N.H. ; writing--original draft preparation, H.N.H. ; writing--review and editing, N.A.D.N. and T.N.T. ; visualization, H.N.H. ; supervision, T.N.T. and K.S. ; project administration, T.N.T. ; funding acquisition, T.N.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Hokkaido University (protocol code: 2010-02, date of approval: 28 July 2010). The animal study protocol was approved by the Institutional Review Board of Hokkaido University (protocol code: 08-0127, date of approval: 18 March 2008). A part of the research was supported by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science. Not applicable. Data underlying the results presented in this paper are not publicly available but may be obtained from the authors upon reasonable request.