Exemplar-based image inpainting using structural feature offsets statistics

Zhidan Li; Jixiang Cheng

doi:10.1117/12.2502934

9 August 2018 Exemplar-based image inpainting using structural feature offsets statistics

Zhidan Li, Jixiang Cheng

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080606 (2018) https://doi.org/10.1117/12.2502934
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

To well maintain structure coherence of a repaired image, an exemplar-based image inpainting algorithm using structural feature offsets statistics is proposed. First, the whole degraded image is partitioned into structural part and non-structural part using Canny operator. Afterwards, the patch offsets statistics of each part are separately calculated and only a few dominant offsets for each part are selected as candidate labels. Finally, global energy function is constructed by simultaneously considering color and gradient information and solved by using multi-label graph cuts. Experimental results show that the proposed method yields generally better inpainted effect than three state-of-the-art methods in terms of structure coherence and neighborhood consistence.

Citation Download Citation

Zhidan Li and Jixiang Cheng "Exemplar-based image inpainting using structural feature offsets statistics", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080606 (9 August 2018); https://doi.org/10.1117/12.2502934

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available