A Criminisi-DnCNN Model-Based Image Inpainting Method

Zun Li, Yuanpei Zhu, Yuping Wang and Toqeer Mahmood

Mathematical Problems in Engineering, 2022, vol. 2022, 1-8

Abstract: Existing image inpainting methods achieve unideal results in dealing with centralized inpainting areas. For this reason, in this study, a Criminisi-DnCNN model-based image inpainting method is proposed. Inspired by the manual inpainting technology, the pointwise mutual information (PMI) algorithm was adopted to obtain the marginal structural map of the images to be repaired. Then, the Criminisi algorithm was used to restore the marginal structure to obtain the complete marginal structure image guided by the superficial linear structure. Finally, the problem of texture inpainting was converted into the counterpart of image denoising through the separation of variables by using the denoising convolutional neural network image denoiser (DnCNN). Compared with the existing inpainting methods, this model has improved the clarity of the marginal structure and reduced the blurring of the area to be repaired.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/mpe/2022/9780668.pdf (application/pdf)
http://downloads.hindawi.com/journals/mpe/2022/9780668.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9780668

DOI: 10.1155/2022/9780668

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().