GAN-based single-image reflectance removal using depth of field guidance

Miran Heo; Yoonsik Choe

doi:10.1117/12.2567028

1 June 2020 GAN-based single-image reflectance removal using depth of field guidance

Miran Heo, Yoonsik Choe

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 1151503 (2020) https://doi.org/10.1117/12.2567028
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

Eliminating reflections on a single-image has been a challenging issue in image processing and computer vision, because defining an elaborate physical model to separate irregular reflections is almost impossible. In fact, while human vision can automatically focus on the transmitted object, basic deep neural networks even have a limitation to learn the attentive mechanism. In this paper, to solve this problem, a Generative Adversarial Networks guided by using Depth of Field (DoF) is proposed. The DoF is formulated by using image statistics and indicates the focused region of image. Thus, by adding this information to both generative and discriminative networks, the generator focuses on the transmitted layer and the discriminator will be able to estimate the local consistency of the restored areas. Since it is intractable to obtain the ground-truth transmitted layer in real images, a dataset with synthetic reflection is considered for quantitative evaluation. The experimental results demonstrate that the proposed method outperforms the existing approaches in both PSNR and SSIM. The visual outputs indicate that the proposed network convincingly eliminates the reflection and produce sufficient transmitted layers as compared to the previous methods.

Citation Download Citation

Miran Heo and Yoonsik Choe "GAN-based single-image reflectance removal using depth of field guidance", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 1151503 (1 June 2020); https://doi.org/10.1117/12.2567028

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Image transmission

Visual process modeling

Reflectivity

Image restoration

Neural networks

Machine learning

Show All Keywords

Keywords/Phrases

Search In:

Publication Years