scholarly journals Color Texture Image Inpainting Using the Non Local CTV Model

2013 ◽  
Vol 04 (03) ◽  
pp. 43-51 ◽  
Author(s):  
Jinming Duan ◽  
Zhenkuan Pan ◽  
Wangquan Liu ◽  
Xue-Cheng Tai
Keyword(s):  
Image Inpainting ◽  
Texture Image ◽  
Non Local ◽  
Color Texture

Fast algorithm for color texture image inpainting using the non-local CTV model

2015 ◽  
Vol 62 (4) ◽  
pp. 853-876 ◽  
Author(s):  
Jinming Duan ◽  
Zhenkuan Pan ◽  
Baochang Zhang ◽  
Wanquan Liu ◽  
Xue-Cheng Tai
Keyword(s):  
Fast Algorithm ◽  
Image Inpainting ◽  
Texture Image ◽  
Non Local ◽  
Color Texture

scholarly journals Non-Local and Multi-Scale Mechanisms for Image Inpainting

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3281
Author(s):  
Xu He ◽  
Yong Yin
Keyword(s):  
Markov Random Fields ◽  
Receptive Fields ◽  
Image Inpainting ◽  
Long Distance ◽  
Visual Appearance ◽  
Fine Grained ◽  
Multi Scale ◽  
Non Local ◽  
Relationship Of

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.

An effective color texture image segmentation algorithm based on hermite transform

2018 ◽  
Vol 67 ◽  
pp. 494-504 ◽  
Author(s):  
Yaman Akbulut ◽  
Yanhui Guo ◽  
Abdulkadir Şengür ◽  
Muzaffer Aslan
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Texture Image ◽  
Hermite Transform ◽  
Texture Image Segmentation ◽  
Image Segmentation Algorithm ◽  
Color Texture

Non-Local Image Inpainting Using Low-Rank Matrix Completion

2014 ◽  
Vol 34 (6) ◽  
pp. 111-122 ◽  
Author(s):  
Wei Li ◽  
Lei Zhao ◽  
Zhijie Lin ◽  
Duanqing Xu ◽  
Dongming Lu
Keyword(s):  
Matrix Completion ◽  
Image Inpainting ◽  
Low Rank ◽  
Rank Matrix ◽  
Non Local ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Local Image
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