A Criminisi-DnCNN Model-Based Image Inpainting Method

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 wa...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-08, Vol.2022, p.1-8
Main Authors: Li, Zun, Zhu, Yuanpei, Wang, Yuping
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Blurring
Deep learning
Image restoration
Mathematical problems
Methods
Noise reduction
Optimization
Partial differential equations
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/9780668