Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image. (5th September 2019)

• Record Type: Journal Article
• Authors: Zhao, Dong; Xu, Long; Chen, Linjie; Yan, Yihua; Duan, Ling-Yu
• Other Names: Musielak Zdzislaw E. Academic Editor.
• Is Part Of: Advances in astronomy. Volume 2019(2019)
• Publication Date: 2019-09-05
• Subjects: Astronomy -- Periodicals; Astronomy; Periodicals; 520
• DOI: 10.1155/2019/5343254
• Languages: English
• ISSNs: 1687-7977

Abstract

Abstract : Overexposure may happen for imaging of solar observation as extremely violet solar bursts occur, which means that signal intensity goes beyond the dynamic range of imaging system of a telescope, resulting in loss of signal. For example, during solar flare, Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) often records overexposed images/videos, resulting loss of fine structures of solar flare. This paper makes effort to retrieve/recover missing information of overexposure by exploiting deep learning for its powerful nonlinear representation which makes it widely used in image reconstruction/restoration. First, a new model, namely, mask-Pix2Pix network, is proposed for overexposure recovery. It is built on a well-known Pix2Pix network of conditional generative adversarial network (cGAN). In addition, a hybrid loss function, including an adversarial loss, a masked L1 loss and a edge mass loss/smoothness, are integrated together for addressing challenges of overexposure relative to conventional image restoration. Moreover, a new database of overexposure is established for training the proposed model. Extensive experimental results demonstrate that the proposed mask-Pix2Pix network can well recover missing information of overexposure and outperforms the state of the arts originally designed for image reconstruction tasks.