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.
Highlights
Digital image inpainting is one of the research hotspots in the field of image restoration, which fills the missing areas with plausible content or replaces the unwanted objects with background utilizing the neighborhood information in digital images
Narrowed the match area by picking out those candidates whose sum of the pixel values is close to the target patch’s, but this cannot guarantee that the bad candidates are excluded; Criminisi’s and related patch-based inpainting techniques require a large number of iterations to completely fill the unknown area, since only one target patch can be filled in a single iteration
A novel multi-patch-based image inpainting algorithm is proposed for filling missing areas in a more accurate and efficient way
Summary
Digital image inpainting is one of the research hotspots in the field of image restoration, which fills the missing areas with plausible content or replaces the unwanted objects with background utilizing the neighborhood information in digital images. 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; patch-based methods can extend the linear structure without introducing blurring defects, and fill more pixels per iteration. We first propose a metric to evaluate the nonuniformity in an image, and; To achieve a more accurate and flexible inpainting, the patch size is adaptively determined according to its nonuniformity; To save the match time, our subregion search strategy allows the match only between patches with similar content This trick helps to narrow down the match area to a large extent while without missing the optimal sample patch, and skips those bad sample patches to avoid mismatch; Appl.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
