## 4 Results and Discussion
### Depth Estimation  
Depth estimation is an essential task for analyzing the properties of absorption structures. This section presents a deep learning approach to estimate the depths of absorption structures from their images. For this purpose, a dataset of 7040 images of absorption structures, each labeled with one of 40 depth values ranging from 0.1 mm to 20 mm, is used. Four state-of-the-art deep learning models, namely ResNet50, VGG16, VGG19, and DenseNet169, are trained and evaluated in this dataset. Accuracy is the evaluation metric that measures the percentage of images whose depth labels are correctly predicted by the models. Table 4 shows the training and validation accuracy of each model after 20 epochs. Table 4 shows that DenseNet169 achieves the highest accuracy in both the training and validation sets, followed by VGG16, VGG19, and ResNet50. All models perform better than in previous experiments with a smaller dataset, indicating the positive impact of dataset size and diversity on model performance. However, the accuracy of all models is still low, indicating the difficulty of the depth estimation task. To further analyze the behavior of the models, the accuracy curves of each model were plotted during training and validation, as shown in Figure 14.  
In Figure 14, the conspicuous features include low accuracy values and pronounced fluctuations, indicating a struggle of the models to glean effective insights from the original 7040-image dataset. This conundrum can be attributed to the inherent complexity of the dataset, characterized by an extensive array of depth classes (40) coupled with a limited count of images per class (fewer than 176 images). Consequently, the models struggled to discern nuanced differentiators across various depth levels, impairing their capacity for comprehensive learning. The fluctuations in accuracy, evident in the jagged trajectory after each epoch, underscored the models' susceptibility to data fluctuations, amplifying the instability quotient. To improve the performance of the models, data augmentation techniques were applied to increase the size and diversity of the dataset. Specifically, angle rotation was used to generate new images from existing ones by randomized 160 different angles and rotating them at angles between 0 and 360 degrees. This resulted in an augmented dataset of 70,400 images (7040 \(\times\) 10) with the same depth labels as before. The decision to employ 160 different angles for image rotation during the data generation process in this study is purposeful and aligned with the goal of improving the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Accuracy** & **Validation Accuracy** \\ \hline ResNet50 & 0.4312 & 0.3921 \\ VGG16 & 0.5124 & 0.4678 \\ VGG19 & 0.4894 & 0.4500 \\ DenseNet169 & 0.7323 & 0.6250 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The training and validation accuracy of each model after 20 epochs. Figure 14: Accuracy evaluation of various models: ResNet50, VGG16, VGG19, and DenseNet169. robustness and generalization capabilities of the trained convolutional neural network (CNN) models. This technique, commonly referred to as data augmentation, serves to simulate varying viewpoints and orientations of the same scene or object, thereby aiding the model in comprehending and identifying features from diverse angles. In the context of estimating depth from blurred images of absorbing structures within a scattering medium, the rationale behind incorporating image rotation at numerous angles can be succinctly summarized:  
* Increased variability: By generating images from multiple angles, the dataset gains greater diversity. This variability acts as a defense against overfitting, ensuring that the model learns broader transferable features instead of memorizing specific training samples. * Robustness to orientation: Real-world scenarios involve objects with varying orientations. Training the model on images spanning different orientations enhances its resilience to changes in object rotation.