Image Inpainting Based on Image Structure and Texture Decomposition

2018 ◽  
pp. 1096-1106
Author(s):  
Zhenping Qiang ◽  
Hui Liu ◽  
Zhenhong Shang
Keyword(s):  
Image Inpainting ◽  
Image Structure

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Vol 34 (07) ◽  
pp. 12605-12612 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures — edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

scholarly journals Image Inpainting Methods Evaluation and Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Raluca Vreja ◽  
Remus Brad
Keyword(s):  
Partial Derivative ◽  
Texture Synthesis ◽  
Image Inpainting ◽  
Digital Processing ◽  
Analytical Comparison ◽  
Image Structure ◽  
Hybrid Techniques ◽  
Methods And Techniques

With the upgrowing of digital processing of images and film archiving, the need for assisted or unsupervised restoration required the development of a series of methods and techniques. Among them, image inpainting is maybe the most impressive and useful. Based on partial derivative equations or texture synthesis, many other hybrid techniques have been proposed recently. The need for an analytical comparison, beside the visual one, urged us to perform the studies shown in the present paper. Starting with an overview of the domain, an evaluation of the five methods was performed using a common benchmark and measuring the PSNR. Conclusions regarding the performance of the investigated algorithms have been presented, categorizing them in function of the restored image structure. Based on these experiments, we have proposed an adaptation of Oliveira’s and Hadhoud’s algorithms, which are performing well on images with natural defects.

Object-centered View Synthesis using Learning-based Image Inpainting

2019 ◽  
pp. 154
Author(s):  
HONG-CHANG SHIN ◽  
Gwangsoon Lee ◽  
Ho min Eum ◽  
Jeong-Il Seo
Keyword(s):  
Image Inpainting ◽  
View Synthesis

Image inpainting algorithm based on double-cross curvature-driven diffusion model

2013 ◽  
Vol 33 (12) ◽  
pp. 3536-3539
Author(s):  
Donghai ZHAI ◽  
Wenjie ZUO ◽  
Weixia DUAN ◽  
Jiang YU ◽  
Tongliang LI
Keyword(s):  
Diffusion Model ◽  
Image Inpainting ◽  
Curvature Driven ◽  
Double Cross

Algorithm for iterative filtering fast image inpainting

2013 ◽  
Vol 26 (3) ◽  
pp. 248-254
Author(s):  
Chun Liu ◽  
Zhiwei Kang ◽  
Yigang He
Keyword(s):  
Image Inpainting ◽  
Iterative Filtering
Sign in / Sign up

Export Citation Format

Share Document