An Effective Content-Aware Image Inpainting Method

Image inpainting is a technique for restoring damaged old photographs and removing undesired objects from an image. The basic idea behind the technique is to automatically fill in lost or broken parts of an image using information from the surrounding area. The challenge of current inpainting algorithms is to restore both texture and structure characteristic information for large and thick damaged regions. This paper presents a new hybrid image inpainting method based on Bezier curves which combines the exemplar-based inpainting technique and the edge-based image restoration algorithm. For restoring image structures, we first use the segmentation result with iterative Otsu’s thresholding to obtain the information of edges. Bezier curves are then used to reconstruct the image skeletons in missing areas. It improves the limitation of the edge-based restoration approach which approximates incoming edges with only lines and circle arcs. The inpainting process is divided into two phases: the first phase restores the image structure by pixel-based interpolation, while the second phase fills holes for preserving texture information with patch-based inpainting method. Experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method. By restoring the curvature structures and textures of the damaged regions, the proposed method achieves content-aware image inpainting.