Application of Image inpainting ranges from object removal, photo restoration, scratch removal, and so on. In this thesis, we will propose a modified multi-scale method and learning-based method using artificial neural networks for image inpainting.
Multi-scale inpainting method combines image segmentation, contour estimation, and exemplar-based inpainting. The main goal of image segmentation is to separate image to several homogeneous regions outside the target region. After image segmentation, we use contour estimation to estimate curves inside the target region to partition the whole image into several different regions. Then we fill those different regions inside the target region separately by exemplar-based inpainting method.
The exemplar-based technique fills the target region via the texture synthesis and filling order of exemplary patches. Exemplary patches are found near target region and the filling order is determined by isophote and densities of exemplary patches.
Learning-based inpainting is a novel technique. This technique combines machine learning and the concept of filling order. We use artificial neural networks
to learn the structure and texture surrounding the target region. After training, we fill the target region according to the filling order.
From our simulation results, very good results can be obtained for removing large-size objects by using the proposed multi-scale method, and for removing medium-size objects of gray images.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0629107-215544 |
| Date | 29 June 2007 |
| Creators | Hsu, Chih-Ting |
| Contributors | Jyh-Horng Jeng, Jer-Guang Hsieh, Rey-Chue Hwang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0629107-215544 |
| Rights | withheld, Copyright information available at source archive |
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