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Super Resolution-based Robust Image Inpainting for Large-scale Missing Regions
Jieun Lee, SeungWon Jung, Jonghwa Shim, Eenjun Hwang
http://doi.org/10.5626/JOK.2022.49.9.708
Image inpainting is a method of filling missing regions of an image with plausible imagery. Even though the performance of recent inpainting methods has been significantly improved owing to the introduction of deep learning, unnatural results can be obtained when an input image has a large-scale missing region, contains a complex scene, or is a high-resolution image. In this study, we propose a super resolution-based two-stage image inpainting method, motivated by the point that inpainting performance in low-resolution images is better than in high-resolution images. In the first step, we convert a high-resolution image into a low-resolution image and then perform image inpainting, which results in the initial output image. In the next step, the initial output image becomes the final output image, with the same resolution as the original input image using the super resolution model. To verify the effectiveness of the proposed method, we conducted quantitative and qualitative evaluations using the high-resolution Urban100 dataset. Furthermore, we analyzed the inpainting performance depending on the size of the missing region and demonstrated that the proposed method could generate satisfactory results in a free-form mask.
Hole-Filling Method for Depth-Image-Based Rendering for which Modified-Patch Matching is Used
Jea-Hyung Cho, Wonseok Song, Hyuk Choi
Depth-image-based rendering is a technique that can be applied in a variety of 3D-display systems. It generates the images that have been captured from virtual viewpoints by using a depth map. However, disoccluded hole-filling problems remain a challenging issue, as a newly exposed area appears in the virtual view. Image inpainting is a popular approach for the filling of the hole region. This paper presents a robust hole-filling method that reduces the error and generates a high quality-virtual view. First, the adaptive-patch size is decided using the color and depth information. Also, a partial filling method for which the patch similarity is used is proposed. These efforts reduce the error occurrence and the propagation. The experiment results show that the proposed method synthesizes the virtual view with a higher visual comfort compared with the existing methods.
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