###### Abstract  
The development of optical sensors, especially with regard to the improved resolution of cameras, has made optical techniques more applicable in medicine and live animal research. Research efforts focus on image signal acquisition, scattering de-blur for acquired images, and the development of image reconstruction algorithms. Rapidly evolving artificial intelligence has enabled the development of techniques for de-blurring and estimating the depth of light-absorbing structures in biological tissues. Although the feasibility of applying deep learning to overcome these problems has been demonstrated in previous studies, limitations still exist in terms of de-blurring capabilities on complex structures and the heterogeneity of turbid medium, as well as the limit of accurate estimation of the depth of absorptive structures in biological tissues (shallower than 15.0 mm). These problems are related to the absorption structure's complexity, the biological tissue's heterogeneity, the training data, and the neural network model itself. This study thoroughly explores how to generate training and testing datasets on different deep learning models to find the model with the best performance. The results of the de-blurred image show that the Attention Res-UNet model has the best de-blurring ability, with a correlation of more than 89% between the de-blurred image and the original structure image. This result comes from adding the Attention gate and the Residual block to the common U-net model structure. The results of the depth estimation show that the DenseNet169 model shows the ability to estimate depth with high accuracy beyond the limit of 20.0 mm. The results of this study once again confirm the feasibility of applying deep learning in transmission image processing to reconstruct clear images and obtain information on the absorbing structure inside biological tissue. This allows the development of subsequent transillumination imaging studies in biological tissues with greater heterogeneity and structural complexity.  
point spread function (PSF); de-blurring; scattering suppression; depth estimation; Attention Res-Unet; DenseNet169; absorbing structure; turbid medium 2023  
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restoration of clear images from blurred images [5; 6; 7; 8; 9; 10; 11; 12]. Optical computed tomography (OCT) utilizing near-infrared light has been proposed and has shown satisfactory results in small animal imaging [7]. Deep learning (CNN) and stacking methods were proposed that were used to estimate the depth and de-blurring transillumination images of a turbid medium [8; 9; 10; 11; 12]. The effectiveness of previous studies is limited to a depth of absorbing structure shallower than 15.0 mm [10; 12].  
This study's models are based on novel machine learning mechanisms that combine different types of neural networks and sparse coding techniques to achieve high-quality image super-resolution [13]. The proposed models are also capable of handling multimodal and cross-domain image processing tasks, such as enhancing images from different sources or modalities, transferring styles or attributes between images, or generating realistic images from sketches or text descriptions [14]. The proposed models are inspired by some of the recent advances in machine learning algorithms and mechanisms for image processing, as well as some of the applications of image processing techniques for machine learning [15]. This study proposes new deep learning models to improve absorbing structures' de-blurring and depth estimation. The following sections of this paper will provide detailed information about the training dataset, the model employed for the de-blurring and depth estimation of absorbing structures, the performance parameters of the training process, and results and discussions concerning the de-blurring and depth estimation of absorbing structures.