TY - GEN
T1 - Compressed Sensing for Astronomical Image Compression and Denoising
AU - Zhang, Jie
AU - Chen, Yibin
AU - Zhang, Huanlong
AU - Shi, Xiaoping
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - In deep exploration, astronomical images are often contaminated by noise when they are transmitted to the earth by satellites. In addition, the existing compression methods are difficult to compress the image with a lower compression sampling ratio, resulting in longer image data transmission time. Donoho proposed a new sampling theory named compressed sensing (CS) in 2006, which can recover a high quality image only using a few information of original image. In this paper, CS method is employed to solve the problem of astronomical image compression, meanwhile, the CS recovery denoising algorithm based curvelet is proposed for astronomical image denoising. The experimental results show that the compression performance of CS method is superior to the famous JPEG and JPEG-2000 compression method, it can compress the high resolution astronomical image with a lower compression sampling ratio. Meanwhile, the proposed algorithm can effectively remove more noise from the noisy image, and preserves more detailed features.
AB - In deep exploration, astronomical images are often contaminated by noise when they are transmitted to the earth by satellites. In addition, the existing compression methods are difficult to compress the image with a lower compression sampling ratio, resulting in longer image data transmission time. Donoho proposed a new sampling theory named compressed sensing (CS) in 2006, which can recover a high quality image only using a few information of original image. In this paper, CS method is employed to solve the problem of astronomical image compression, meanwhile, the CS recovery denoising algorithm based curvelet is proposed for astronomical image denoising. The experimental results show that the compression performance of CS method is superior to the famous JPEG and JPEG-2000 compression method, it can compress the high resolution astronomical image with a lower compression sampling ratio. Meanwhile, the proposed algorithm can effectively remove more noise from the noisy image, and preserves more detailed features.
KW - Compressed sensing
KW - astronomical image
KW - compression sampling ratio
KW - curvelet
KW - denoising
UR - https://www.scopus.com/pages/publications/85091558696
U2 - 10.1109/CCDC49329.2020.9164087
DO - 10.1109/CCDC49329.2020.9164087
M3 - 会议稿件
AN - SCOPUS:85091558696
T3 - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
SP - 1162
EP - 1167
BT - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd Chinese Control and Decision Conference, CCDC 2020
Y2 - 22 August 2020 through 24 August 2020
ER -