# Image Inpainting Based on Multi-Patch Match with Adaptive Size
###### Abstract  
Patch-based image inpainting methods iteratively fill the missing region via searching the best sample patch from the source region. However, most of the existing approaches basically use the fixed size of patch regardless of content features nearby, which may lead to inpainting defects. Also, global match is needed for searching the best sample patch, but only to fill one target patch in each iteration, resulting in low efficiency. To handle the issues above, we first evaluate the nonuniformity in an image, by which the patch size is adaptively determined. Moreover, we divide the source region into multiple non-overlapping subregions with different nonuniformity levels, and the patch match proceeds in every subregion, respectively. This strategy not only saves the match time for single target patch, but also reduces the mismatch, and enables the simultaneous filling of multiple target patches in a single iteration. Experimental results show that in comparison to previous patch-based works, our method has achieved further improvement both in quality and efficiency. We believe our method could provide a new way for patch match with better accuracy and efficiency in image inpainting tasks. image inpainting; nonuniformity; adaptive patch size; subregion search; multi-patch match +
Footnote †: journal:  
an energy functional established by the incomplete image. The TV model only relies on the gradient value of the isophote, rather than the geometric information, and always tends to use the shortest straight line to connect broken linear structures, which cannot satisfy the principle of visual connectivity. Later, based on TV model, [PERSON] et al. [4] developed an improved curvature driven diffusion model (a.k.a. CDD model) to control the diffusion intensity, the CDD model has made up for the shortcomings of the TV model. [PERSON] et al. [5] filtered the missing areas with Gaussian convolution kernel to diffuse the known pixel information into the unknown region. Inpainting algorithms based on PDE could maintain the linear structure properly. However, these methods can only fill a very small number of pixels each iteration, and it still takes a long time even if to fill a small missing area. Although Gaussian-convolution based method is simple and fast, it fails to hold the linear structure. Generally, diffusion-based methods can only obtain better effect when the missing area is small, when it comes with a larger missing area, these methods may introduce visual blurring defects as the filling progresses, resulting in low inpainting quality. In order to solve the problem that diffusion-based methods are more likely to introduce blurring defects when dealing with larger missing area, another category, the patch-based methods were proposed on the basis of the related research of texture synthesis and stitching [6; 7; 8; 9]. The central idea is to select an appropriate sample texture patch from the source region to fill the unknown region under the certain rules. Because these algorithms use the texture patch as the filling unit rather than the single pixel, they could capture the local texture features better; therefore, patch-based methods can extend the linear structure without introducing blurring defects, and fill more pixels per iteration. Generally speaking, patch-based methods outperform diffusion-based methods in both quality and efficiency, and are commonly used in image inpainting. One of the most representative pioneer works of patch-based methods is the exemplar-based image inpainting algorithm proposed by [PERSON] et al. [10], which has also become the baseline work of other related patch-based methods. These methods mainly focus on two issues: One is to improve the filling order [11; 12; 13; 14], the other is to find a better patch match criterion [15; 16; 17] (see Section 2 for details)--and these two issues are almost tackled. In addition to this, [PERSON] et al.