Abstract
Image inpainting has been presented to complete missing content according to the content of the known region. This paper proposes a novel and efficient algorithm for image inpainting based on a surface fitting as the prior knowledge and an angle-aware patch matching. Meanwhile, we introduce a Jaccard similarity coefficient to advance the matching precision between patches. And to decrease the workload, we select the sizes of target patches and source patches dynamically. Instead of just selecting one source patch, we search for multiple source patches globally by the angle-aware rotation strategy to maintain the consistency of the structures and textures. We apply the proposed method to restore multiple missing blocks and large holes as well as object removal tasks. Experimental results demonstrate that the proposed method outperforms many current state-of-the-art methods in patch matching and structure completion.
Highlights
With the advancement of society and the rapid development of the Internet, image completion, called image inpainting, has been applied to many fields proverbially, such as the protection of ancient relics, image editing, medical field, and military field
6 Conclusions In this paper, we have proposed a novel inpainting method which estimates the missing pixels by moving least squares (MLS) method in 3D subspace as the prior knowledge utilized to solve the confidence term
We propose an angle-ware rotation patch matching strategy which considers the different angles of the same source patch in order to find multiple candidate patches for every target patch, thereby increasing the matching accuracy
Summary
With the advancement of society and the rapid development of the Internet, image completion, called image inpainting, has been applied to many fields proverbially, such as the protection of ancient relics, image editing, medical field, and military field. The second category is exemplar-based methods which have been presented to complete an image with a large missing region [9–13]. Exemplarbased inpainting methods filled in the missing region at a pixel level or a patch level. Due to massive runtime and inconsistent texture synthesis at a pixel level, Efors and Leung [9] proposed a patch-based inpainting method to fill in the unknown region by using texture synthesis. Criminisi et al [10] presented a classical inpainting method to remove a large object according to the computation of priority and similarity of patches, while preserving important information of texture and structure. The proposed approach can select multiple matching patches automatically from the available region by using an angle-aware rotation strategy to increase the probability of obtaining the optimal matching patch. After fillingΨp^, the boundary δΩ is updated iteratively until the unknown region is filled entirely
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