Abstract
Image inpainting is a widely used technique for image repairing, which utilizes information from the undamaged regions in the same image to do the inpainting. However, the repairing methods using inpainting cannot work well when the textures in the damaged area cannot be found in the remaining area of the image. In this paper, an image repairing method is presented, which uses Affine transformation and Poisson blending for repairing, based on the assumption that reference images are available. The reference image is first affine-transformed to have the same viewing angle and scaling size to the damaged image and then Poisson blending is applied to repair the damaged area by compositing the corresponding area in the affined reference image. Since the blending will preserve reference image’s textures and adjust its illumination and color tone according to the damaged image, the composition is supposed to be seamlessly. However, it was observed that using Poisson blending in image repairing may cause dramatically blurring in the resulting image sometimes. This mainly comes from the big differences in pixel-gradient changes in the blending area. To cope with the problem, a region-based Poisson blending is proposed, in which the damaged area is not blended as a whole, but segmented into regions and applied with blending separately. The experimental result shows that the blurring artifacts can be reduced obviously by using the proposed region-based approach.
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Chen, WC., Tsai, WJ. (2019). Region-Based Poisson Blending for Image Repairing. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_30
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DOI: https://doi.org/10.1007/978-3-030-01054-6_30
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