## 2 Materials and Methods
### Data Preparation
In the given context, specific \(\mu_{s}^{\prime}\) and \(\mu_{a}\) values might be set to simulate tissue optical properties in a model. These values depend on the type of tissue, the wavelength of light, and the experimental conditions. Setting these values likely aims to ensure that simulations closely mimic real tissue optical behavior under specific circumstances. Although the specific rationale hinges on precise values, the choice of these parameters is vital to accurately model light-tissue interactions and align simulation outcomes with experimental data. In studies involving optical imaging and simulations of light propagation in biological tissues, setting the values of \(\mu_{s}^{\prime}\) (reduced scattering coefficient) and \(\mu_{a}\) (absorption coefficient) is a critical step. Researchers usually consider the following factors when determining \(\mu_{s}^{\prime}\) and \(\mu_{a}^{\prime}\) values:  
* Empirical data: Experimental measurements of optical properties in specific tissue types at various wavelengths can serve as a foundation for determining the appropriate values. These measurements can come from the literature or new measurements conducted by the researchers themselves. * Literature references: Previous studies often report ranges or specific values of \(\mu_{s}\) and \(\mu_{a}\) for similar tissue types. Researchers can use these references as a starting point and adjust the values based on their experimental setup. * Theoretical models: There are established theoretical models that relate optical properties to tissue composition and structure. Researchers can leverage these models to estimate \(\mu_{s}\) and \(\mu_{a}\) based on the known components and concentrations in the tissue. * Tissue variation: Different tissues exhibit different optical properties as a result of variations in cellular composition, structure, and pigmentation. Consequently, the specific tissue under investigation must be carefully considered when selecting \(\mu_{s}\) and \(\mu_{a}\) values. * Wavelength Dependence: Optical properties can vary with the wavelengths of light. Researchers may choose \(\mu_{s}\) and \(\mu_{a}\) values that align with the wavelength range used in their experimental setup. * Validation: Validating the chosen values involves comparing the simulation results with actual experimental observations. If the simulated outcomes closely match the experimental data, this provides confidence in the suitability of the parameter values. * Sensitivity analysis: Researchers may conduct sensitivity analyses to assess how changes in \(\mu_{s}\) and \(\mu_{a}\) impact simulation results. This analysis helps to determine reasonable ranges for these parameters. In essence, substantiating the selections of \(\mu_{s}\) and \(\mu_{a}\) values typically entails a combination of empirical data, theoretical frameworks, references from the literature, and validation against experimental results. The specific strategy can be flexible to existing resources, unique tissue attributes, and the specific objectives of the experiment. For the de-blurring study, a comprehensive dataset consisting of 8000 pairs of clear and blurred images was generated by convoluting 10 of 12 original structures with the PSF given by Equation (1) at depths ranging from 0.1 to 20.0 mm (interval 0.1 mm) and then rotating at four different angles, as illustrated in Table 1. The remaining 2 of the 12 original structures were used to generate data for testing. During the training process for de-blurring, the generated dataset was used to train the models with a batch size of 8. The learning rate was set to \(10^{-4}\), and the models were trained for 100 epochs. For the depth estimation study, the corresponding depth labels associated with the blurry images were used. A dataset of 70,400 images was generated that depicts the absorbing structures within the scattering medium at different depths.