# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 5 Conclusions and Future Work  
In this study, we proposed an integrated framework for image inpainting and colorization of arbitrarily masked grayscale images. By leveraging the strengths of state-of-the-art models--namely, the segment anything model (SAM), the mask-aware transformer model (MAT), and DDColor--our approach demonstrated robust performance in handling complex restoration tasks. Through extensive qualitative and quantitative evaluations on datasets such as DACON and Places365-Standard, our method consistently outperformed baseline approaches in structural continuity, textural detail preservation, and natural colorization.  
Our findings highlight several key contributions. First, the seamless integration of SAM, MAT, and DDColor into a unified pipeline effectively addresses challenges associated with arbitrary mask shapes and diverse content. Second, the framework not only restores missing regions with high structural fidelity but also produces visually coherent colorized outputs, showcasing its practical applicability in real-world scenarios. Finally, the intro  
Figure 5: Limitations of the framework. The examples show challenging cases where our method may produce suboptimal results, particularly in scenarios involving complex geometric structures or extreme lighting conditions.  
duced comprehensive evaluation metric provided a balanced assessment of restoration quality, underscoring the framework's ability to deliver superior results.  
Despite these achievements, there are certain limitations that present opportunities for future work. The framework struggles with highly detailed or repetitive patterns, complex geometric structures, and extreme lighting conditions. Addressing these issues will require further advancements in contextual understanding and robustness. Future research could explore the integration of more advanced transformer-based architectures or self-supervised pre-training to enhance the framework's adaptability to challenging scenarios. Additionally, incorporating user feedback during restoration process could improve both accuracy and usability.  
In conclusion, the proposed framework represents a significant step forward in the field of image restoration and colorization; while there is room for improvement, the results affirm the potential of integrating specialized deep learning models to address complex image processing challenges. We believe that continued research in this direction will yield even more effective and versatile solutions.  
Conceptualization, W.-H.Y. and H.-C.R.; methodology, W.-H.Y. and H.-C.R.; software, W.-H.Y. and H.-C.R.; validation, W.-H.Y. and H.-C.R., writing--original draft preparation, W.-H.Y.; writing--review and editing, H.-C.R.; supervision, H.-C.R.; project administration, H.-C.R., funding acquisition, H.-C.R. All authors have read and agreed to the published version of the manuscript. This paper was supported by the Academic Research Fund of Dr. [PERSON] in 2023.  
Not applicable.  
The data presented in this study are openly available in GitHub at [[https://github.com/unacanal/inpaint-color](https://github.com/unacanal/inpaint-color)]([https://github.com/unacanal/inpaint-color](https://github.com/unacanal/inpaint-color)) (accessed on 10 January 2025). The authors declare no conflicts of interest.