Image inpainting is a challenging reconstruction of the damaged image in photography, especially for more valued artwork than before. The damages are mostly caused by scratches and worn out, so they can not be easily fixed physically. Thus, many scientists proposed sophisticated methods for restoring the damaged image into a new one similar to an original image. However, these methods have not solved the problem effectively if the missing region is large. In this paper, we focus on how to restore a large missing region in image inpainting. This algorithm is composed of two steps: structure propagation and color propagation. In structure propagation, we segment a large region (non-homogeneous) into several small regions (homogeneous) based on the salient structure of missing region. Then, we applied a simple pixel-based inpainting method called the Fast Marching Method (FMM) to fill in the missing homogeneous regions by color propagation. In the experimental section, we applied several kinds of missing regions, such as irregular and regular missing regions, in large sizes. The results show that our proposed method performs well in various conditions. INDEX TERMSImage inpainting, user interaction, structure propagation, color propagation, fast marching method (FMM).

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Image completion techniques have made rapid and impressive progress due to advancements in deep learning and traditional patch-based approaches. The surrounding regions of a hole played a crucial role in repairing missing areas during the restoration process. However, large holes could result in suboptimal restoration outcomes due to complex textures causing significant changes in color gradations. As a result, they led to errors such as color discrepancies, blurriness, artifacts, and unnatural colors. Additionally, recent image completion approaches focused mainly on scenery and face images with fewer textures. Given these observations, we present a structure-texture consistent completion approach for filling large holes with detailed textures. Our method focuses on improving image completion in the context of artworks, which are expressions of creativity and often have more diverse structures and textures from applying paint to a surface using brush strokes. To handle the unique challenges posed by artwork, we segment non-homogeneous areas and then use Cohesive Laplacian Fusion to complete the texture of the remaining missing segmented area. This technique involves detecting changes in base structures and textures using multiple matched patches to achieve more consistent results. The experimental results show that our proposed method is competitive and outperforms state-of-the-art methods in missing regions and color gradations of art paintings.

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