Robust Non-Local Total Variation Image Inpainting

2015 ◽  
Author(s):  
Jyothisha J. Nair ◽  
Dhanya Francis
Keyword(s):  
Total Variation ◽  
Image Inpainting ◽  
Non Local

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.

A non local total variation based on L1 norm for image recovery

2017 ◽  
Vol 32 (8) ◽  
pp. 635-641
Author(s):  
杨平先 YANG Ping-xian ◽  
陈明举 CHEN Ming-ju
Keyword(s):  
Total Variation ◽  
Image Recovery ◽  
L1 Norm ◽  
Non Local

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Phaneendra K. Yalavarthy ◽  
Sandeep Kumar Kalva ◽  
Manojit Pramanik ◽  
Jaya Prakash
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Local Means ◽  
Non Local
Sign in / Sign up

Export Citation Format

Share Document