# Image Inpainting Based on Multi-Patch Match with Adaptive Size
## 5 Conclusions and Future Work  
In this paper, a novel multi-patch-based image inpainting algorithm is proposed for filling missing areas in a more accurate and efficient way. Aiming at the shortcomings that traditional patch-based methods use a fixed size of patch and a global search for finding the best sample patch, and only one target patch can be filled in a single iteration, which is computationally expensive, we provide a novel solution: We first introduce a measurement model to quantify the nonuniformity in an image, then different sizes of patches are adaptively determined for regions with different nonuniformity levels, making the restored textures and structures more coherent and natural so that inpainting quality is improved. Moreover, by fully utilizing the nonuniformity, the source region is divided into multiple non-overlapping subregions with different nonuniformity levels; and in each subregion, the best sample patch is matched for target patch. This has successfully reduced the match time in a single iteration and the total number of iterations, as well as the rate of mismatch. Experimental results show that our improved algorithm has obtained better inpainting quality, both subjectively and objectively with less time-consuming.  
In addition, in terms of the inpainting quality, if related works are combined, such as improved priority model and match criterion, the results may become better. In terms of inpainting efficiency, further improvements can be achieved based on the acceleration strategy mentioned in this article. For example, the scalar value of nonuniformity used in this article may contain limited information, other features, such as texture directions, colors, etc., can also be introduced in the process of narrowing the search area to achieve further acceleration.  
Finally, there are also limitations in our algorithm and other patch-based inpainting algorithms. These methods assume that the texture in the missing region can be found elsewhere in the source region. However, this assumption does not always hold--once the missing information is locally unique, similar structures cannot be found, these methods may struggle at reconstructing satisfactory results. Fortunately, in recent years, deep learning techniques have been introduced, and hopefully they are capable of making up for this deficiency. For example, [PERSON] et al. [25] utilized two-stage GAN that has achieved impressive results. Image inpainting based on deep learning techniques might be a novel and robust way--especially in those complex cases and are worth further study.  
Conceptualization, S.Y. and H.L.; Data curation, S.Y.; Formal analysis, H.C.; Investigation, S.Y. and H.L.; Methodology, S.Y.; Project administration, Y.W. and X.C.; Software, S.Y.; Supervision, H.C. and X.C.; Validation, S.Y. and H.L.; Writing--original draft, S.Y., H.L., Y.W. and X.C.; Writing--review & editing, S.Y., H.C. and X.C. All authors have read and agreed to the published version of the manuscript. The research was funded by the Tianjin Municipal Transportation Commission Science and Technology Development Plan Project (2019C-05). The authors would like to thank [PERSON] and [PERSON] et al. for the open-access dataset they provided. The authors declare no conflict of interest.