TY - GEN
T1 - A mosaic method for multichannel sequence starry images via multiscale edge-preserving spatio-temporal context filtering
AU - Yang, Zhijia
AU - Liu, Xiaozheng
AU - Mao, Yuxuan
AU - Zhang, Tinghua
AU - Gao, Kun
N1 - Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - Astronomical observation and spatial target surveillance applications often require mosaic processing of starry images acquired by multiple image sensors to expand the Fields of View (FOV) or improve the resolutions. Due to the low SNR (Signal-to-Noise Ratio), lack of star point texture information and vulnerability of atmospheric turbulence of the starry image properties, traditional mosaic methods are prone to failures during feature point extraction. In this paper, Spatio-Temporal Context (STC) filtering is introduced as the preprocessing procedure to suppress the background interferences. We have improved the classical STC filtering and expands it into multi-scale space combining with Rolling-Guidance Filtering Algorithm (RGFA). Making full use of the fine edge-preserving feature of RGFA, the time-variant or spatial variant interference and noise in the background, such as glimmer stars, night clouds, sensor response noise, etc, are suppressed while the profiles of the target star points are enhanced and easy to extract their centroids. Then, we produced the feature description of the star-point sets via threshold segmentation and morphological algorithms based on geometric invariant cost function for the input image pairs to be stitched. After Random Sample Consensus (RANSAC) processing, the mismatched feature point pairs in the star-point sets are excluded. The subsequent procedures of the registration parameter calculation, image fusion and parallax correction processing are adopted to complete the mosaic processing. The results of digital simulation and practical processing show that the proposed method for the multichannel sequence starry images with the low SNR and complex backgrounds can extract feature points more precisely and more robustly comparing with the traditional methods. So, it is suitable for the large FOV spatial observation or surveillance applications.
AB - Astronomical observation and spatial target surveillance applications often require mosaic processing of starry images acquired by multiple image sensors to expand the Fields of View (FOV) or improve the resolutions. Due to the low SNR (Signal-to-Noise Ratio), lack of star point texture information and vulnerability of atmospheric turbulence of the starry image properties, traditional mosaic methods are prone to failures during feature point extraction. In this paper, Spatio-Temporal Context (STC) filtering is introduced as the preprocessing procedure to suppress the background interferences. We have improved the classical STC filtering and expands it into multi-scale space combining with Rolling-Guidance Filtering Algorithm (RGFA). Making full use of the fine edge-preserving feature of RGFA, the time-variant or spatial variant interference and noise in the background, such as glimmer stars, night clouds, sensor response noise, etc, are suppressed while the profiles of the target star points are enhanced and easy to extract their centroids. Then, we produced the feature description of the star-point sets via threshold segmentation and morphological algorithms based on geometric invariant cost function for the input image pairs to be stitched. After Random Sample Consensus (RANSAC) processing, the mismatched feature point pairs in the star-point sets are excluded. The subsequent procedures of the registration parameter calculation, image fusion and parallax correction processing are adopted to complete the mosaic processing. The results of digital simulation and practical processing show that the proposed method for the multichannel sequence starry images with the low SNR and complex backgrounds can extract feature points more precisely and more robustly comparing with the traditional methods. So, it is suitable for the large FOV spatial observation or surveillance applications.
KW - Edge-Preserving Filter
KW - Geometric Invariant Features
KW - Image Sequence Mosaic
KW - Spatio-Temporal Context
KW - Star Background
UR - http://www.scopus.com/inward/record.url?scp=85082599782&partnerID=8YFLogxK
U2 - 10.1117/12.2550260
DO - 10.1117/12.2550260
M3 - Conference contribution
AN - SCOPUS:85082599782
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - 2019 International Conference on Optical Instruments and Technology
A2 - Situ, Guohai
A2 - Cao, Xun
A2 - Osten, Wolfgang
PB - SPIE
T2 - 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Y2 - 26 October 2019 through 28 October 2019
ER -