Image Demosaicing Based on Generative Adversarial Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-16
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel.
Therefore, noise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image.
In this paper, we proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images.
The proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the computational complexity.
The generator of the GAN is designed using the U-net to directly generate the demosaicing images.
The dense residual network is used for the discriminator to improve the discriminant ability of the network.
We compared the proposed method with several interpolation-based algorithms and the DnCNN.
Results from the comparative experiments proved that the proposed method can more effectively eliminate the image artifacts and can better recover the color image.
American Psychological Association (APA)
Luo, Jingrui& Wang, Jie. 2020. Image Demosaicing Based on Generative Adversarial Network. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1197947
Modern Language Association (MLA)
Luo, Jingrui& Wang, Jie. Image Demosaicing Based on Generative Adversarial Network. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1197947
American Medical Association (AMA)
Luo, Jingrui& Wang, Jie. Image Demosaicing Based on Generative Adversarial Network. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1197947
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197947