# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 1 Introduction  
Image restoration has long been a critical task in computer vision, encompassing challenges such as inpainting and colorization. Inpainting involves reconstructing missing or corrupted regions of an image, while colorization focuses on mapping grayscale images to realistic color spaces. These tasks are not only essential for applications like photo editing and archival restoration but also serve as foundational tools for downstream tasks such as object recognition and scene understanding.  
Traditional approaches often address inpainting and colorization as separate tasks, relying on distinct methodologies and datasets. However, real-world scenarios frequently demand solutions capable of simultaneously handling both tasks, particularly in cases involving arbitrarily shaped masked regions. Existing methods struggle to maintain structural coherence during inpainting while ensuring realistic and context-aware colorization, especially when faced with complex patterns, diverse textures, or challenging lighting conditions.  
To address these challenges, we propose an integrated framework that unifies inpainting and colorization into a cohesive pipeline. Our framework leverages the following three state-of-the-art models: the segment anything model (SAM) [1], the mask-aware transformer (MAT) [2], and the dual-decoder colorization model (DDColor) [3] model.  
SAM provides robust and flexible mask generation for arbitrary regions, MAT ensures high-fidelity inpainting by reconstructing structural and textural details, and DDColor enhances the restored grayscale images with vivid and natural colorization. By combining the strengths of these models, our approach effectively bridges the gap between inpainting and colorization tasks.  
We validate the effectiveness of our framework through extensive qualitative and quantitative experiments on challenging datasets, including DACON [4] and Places365-Standard [5]. The results demonstrate that our method not only outperforms baseline approaches in terms of structural continuity, detail preservation, and natural coloration but also offers a scalable and generalizable solution to complex image restoration problems. The source code for our proposed framework is available at [[https://github.com/unacanal/input-color](https://github.com/unacanal/input-color)]([https://github.com/unacanal/input-color](https://github.com/unacanal/input-color)) (accessed on 10 January 2025).  
The main contributions of this paper are summarized as follows:  
* We propose a novel integrated pipeline that seamlessly combines inpainting and colorization, addressing arbitrary masked regions in grayscale images.
* Our framework leverages state-of-the-art models, including the segment anything model (SAM) for mask generation, the mask-aware transformer model (MAT) for inpainting, and the dual-decoder colorization model (DDColor) for colorization, to achieve superior restoration performance.
* By integrating these models, we achieve improved structural coherence and natural color distribution, compared to existing methods.
* We conduct extensive qualitative and quantitative experiments on multiple benchmark datasets, demonstrating significant performance improvements over prior approaches.  
The remainder of this paper is structured as follows: Section 2 reviews related work, highlighting recent advancements in inpainting and colorization. Section 3 details the methodology of the proposed framework, including its key components and implementation. Section 4 presents experimental results and discussions, showcasing the framework's performance across various scenarios. Finally, Section 5 concludes the paper and outlines potential directions for future work.