Structure guided image completion using texture synthesis and region segmentation

Chen, Qiaochuan; Li, Guangyao; Xie, Li; Xiao, Qingguo; Xiao, Mang

doi:10.1016/j.ijleo.2019.04.028

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…According to the different generation methods, the methods of synthesizing texture can be divided into three categories: texture mapping synthesis method, process-based synthesis method, and sample image-based synthesis method [18]. Texture mapping is to cover any plane figure or image on the surface of the object to form a real color pattern on the surface of the object, which is mainly used to deal with the surface aliasing.…”

Section: Analysis On Cnn and Image Texture (1) Cnn Under Deep Learningmentioning

confidence: 99%

Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

2022

FAR

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This study is to explore the effect of deep learning algorithms applied to the non-photorealistic rendering (NPR) of ink painting. The powerful processing capabilities of NPR technology and convolutional neural networks (CNN) in computer vision are analyzed. Aiming at the features of ink texture images, a new sample-based ink texture synthesis method is proposed. The feature representation of ink texture is calculated using the CNN model, and then an ink texture with the same feature representation is generated on a random image. In addition, a synthesis method of ink painting picture is proposed based on the CNN. The results show that the ink texture generated by the ink texture synthesis algorithm is very good, and the Visual Geometry Group Network (VGG-Net) model shows the best generation effect. For images generated by high-level networks lack a lot of pixel information, but retain the main feature information of the image. When the learning rate is 10, the effect of the ink image synthesized based on the content image is slightly worse; and the image generated by the fusion of multiple styles confirms the feasibility of synthesizing ink images of different "sizes and styles". Therefore, this ink image synthesized on the basis of the CNN model shows a better effect, which extends the possibility of computer creation of richer ink painting works.

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Section: Analysis On Cnn and Image Texture (1) Cnn Under Deep Learningmentioning

confidence: 99%

Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

2022

FAR

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A smart brain MR image completion method guided by synthetic-CT-based multimodal registration

Xia

Zheng

Qian

2019

J Ambient Intell Human Comput

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Structure guided image completion using texture synthesis and region segmentation

Cited by 2 publications

References 29 publications

Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

Deep Learning in Computer Real-time Graphics and Image Using the Visual Effects of Non-photorealistic Rendering of Ink Painting

A smart brain MR image completion method guided by synthetic-CT-based multimodal registration

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