## 1 Introduction
The red arrow represents the direction of the light source and the blue arrow represents the direction of the light reaching the camera during image acquisition. surface. The distribution of light at the observing surface involves the decomposition of incident light into two components: direct light, which undergoes scattering and absorption within the surrounding medium, and diffused light. [PERSON] et al. have successfully applied this PSF to imaging absorption structures in biological tissues, assuming a uniform distribution of light in the plane containing the absorption structures [25]. As a result, [PERSON] et al. have demonstrated optical computed tomography (OCT) with transillumination imaging and have reconstructed internal structures in small animals [25]. However, the scattering suppression process in this study depends on the deconvolution process with the Lucy-Richardson algorithm. Therefore, the restored image depends on the selection of the iteration number. The use of the depth-dependent point spread function in conjunction with deep learning to suppress scattering is one of the most remarkable advances that has been made. [PERSON] et al. developed the scattering suppression technique and the estimation of the depth of the structure in a turbid medium using deep learning [27]. The research team has succeeded in suppressing scattering and estimating depth using convolutional neural network (CNN) and fully convolutional network (FCN) models. Using the proposed technique from [PERSON] et al., the de-blurred images, depth information, and three-dimensional (3D) structure of simple or single absorption structure were estimated [27]. The blood flow in the reconstructed 3D vessels could be estimated using a depth-dependent contrast model [29]. [PERSON] et al. recently proposed novel techniques to reconstruct a 3D structure in a turbid medium from a single blurred 2D image obtained using near-infrared transillumination imaging [30]. One technique uses 1D information on the intensity profile in the light-absorbing image. Profiles under different conditions are calculated by convolution with the depth-dependent point spread function (PSF) of the transillumination image. In databanks, profiles are stored as lookup tables to connect the contrast and spread of the profile to the absorber depth. A one-to-one correspondence was found from the contrast and spread to the absorber depth and thickness. Another technique uses 2D information from the transillumination image of a volumetric absorber. A blurred 2D image is deconvolved with the depth-dependent PSF, thereby producing many images with points of focus on different parts. The depth of the image part can be estimated by searching the deconvolved images for the image part with the best focus. The techniques are time-consuming because of the nature of the convolution and deconvolution process. In addition, it could be applied to the simple structure. The results in previous studies still show limitations when processing a depth of 15.0 mm related to the efficiency of scattering suppression, the shape of the reconstructed structures, the estimated depth, and the applicability in complex structures [25; 26; 28; 29; 30]. These problems are related to the absorption structure's complexity, the biological tissue's heterogeneity, the training data, and the neural network model itself. [PERSON] et al. have proposed the Attention Res-UNet model for de-blurring by adding the Attention gate and the Residual block to the common U-net model structure. As a result, a correlation of more than 89% could be achieved between the de-blurred image and the original structure image [31]. [PERSON] et al. have proposed depth estimation using the DenseNet169 model with high accuracy beyond the limit of 20.0 mm [31]. The complexity of the light-absorbing structure is also unresolved. The current solution is to subdivide the image into several parts, each of which contains only one simple structural part with a relative spatial location roughly on the same plane [27; 29; 30].