## 4 Conclusions  
This study introduces a novel method for disparity estimation in Holoscopic 3D imaging, leveraging angular information from Elemental Images (EIs) over traditional spatial data from Viewpoint Images (VPIs). Our goal was to evaluate if EIs could serve as a more accurate foundation for disparity estimation, offering an alternative to conventional methods.  
Through detailed experimentation, we developed an approach that not only generates accurate disparity maps from EIs but also surpasses both traditional strategies and advanced deep learning algorithms. This achievement stems from an innovative application of the Semi-Global Block Matching (SGBM) method, enhanced by multi-resolution techniques and content-aware analysis, optimizing the use of EIs' angular data.  
Our findings indicate that EIs, even without extensive manipulation, provide a more reliable basis for disparity estimation than VPIs. This suggests that the inherent angular information in EIs is better suited for precise disparity assessments, paving the way for advancements in Holoscopic 3D imaging technology.  
Furthermore, our method outperformed comparable deep learning models, a result attributable to EIs' unique characteristics, notably their lower texture and resolution. This highlights a critical gap in current deep learning approaches: the lack of training on datasets specifically designed for the nuances of EIs. Our study underscores the urgent need to create comprehensive EI datasets for training deep learning models for EI-based applications, promising significant progress in automated disparity estimation.  
Moreover, our investigation into EI resolutions revealed that higher resolutions do not always equate to a better disparity estimation accuracy. We identified an optimal resolution range for EIs, challenging the assumption that higher is always better and offering insights into how resolution influences angular information extraction for disparity calculations.  
Additionally, the potential of EIs for developing compact depth-sensing devices opens new possibilities for their use in microscopes, mobile devices, medical instruments like endoscopes, and real-time depth estimation applications such as autonomous vehicles and medical diagnostics. This is due to EIs' ability to be captured directly by image sensors, simplifying the depth estimation process.  
In conclusion, our research confirms the viability of using angular perspective data from EIs for disparity estimation in Holoscopic 3D imaging, marking a significant advance  
Figure 24: The bar graph depicts the comparative performance of the extracted disparity to that of the methods of [PERSON] et al. [22] and [PERSON] and [PERSON] [23], demonstrating that our method outperforms both methods’ algorithms.  
ment over traditional methods. The implications for future 3D imaging technologies are vast, necessitating continued research to unlock the full potential of EIs in enhancing the depth estimation accuracy and efficiency across various applications.  
Conceptualisation, M.R.S.; methodology, B.A.; software, B.A.; validation, B.A., H.M. and M.R.S.; formal analysis, B.A.; investigation, B.A.; resources, B.A.; data curation, B.A.; writing--original draft preparation, B.A.; writing--review and editing, B.A. and H.M.; visualisation, B.A.; supervision, H.M.; project administration, B.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author, [PERSON], at [EMAIL_ADDRESS]. The data are not publicly available due to commercial privacy.  
The authors declare no conflicts of interest.