Abstract
Image completion is an approach to fill a damaged region (hole) in an image. In this study, we adopt a novel method which can repair a target region with structural constraints in an architectural scene. An objective function that consists of three terms is proposed to solve the image completion problem. In color term, we compute a parameterized transformation model using detected plane parameters and measure the distance between the target patch and transformed source patch. This model helps to extend the patch search space and find an optimal solution. To improve the patch matching accuracy, we add a guide term that includes structure term and consistency term. The structure term encourages sampling patches along the structural direction, and the consistency term is used to maintain the texture consistency. Considering the color deviation between patches, we add a gradient term into a framework that can solve more challenging problems. Compared with previous methods, the proposed method has good performance in preserving global structure and reasonably estimating perspective distortions. Moreover, we obtain acceptable results in natural scenes. The experimental results illustrate that this novel method is a potential tool for image completion.
Highlights
Image completion methods aim to repair the defects of digital images with plausibly synthesized content to make images look more natural
3 Objective function To achieve a high-quality result, we develop an objective function for image completion
The time of the proposed image completion method can be categorized into two cases
Summary
Image completion methods aim to repair the defects of digital images with plausibly synthesized content to make images look more natural. This task is applied to many image editing applications ranging from object removal to movie clip and image understanding [1–3]. Bertalmio et al [4] first proposed a method in which the information was propagated through the edge of a contour line in the occlusion area. These methods have two types: Euler’s model [5] and total variation model [6]. They perform well in the images with thin cracks and scratches; they are not suitable for large damaged regions
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