A Mosaic Method for Multichannel Sequence Starry Images via Multiscale Edge-preserving Spatio-Temporal Context Filtering

摘要

Astronomical observation and spatial target surveillance applications often require mosaic processing of starry imagesacquired 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 starryimage 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-GuidanceFiltering Algorithm (RGFA). Making full use of the fine edge-preserving feature of RGFA, the time-variant or spatialvariantinterference and noise in the background, such as glimmer stars, night clouds, sensor response noise, etc, aresuppressed while the profiles of the target star points are enhanced and easy to extract their centroids. Then, we producedthe feature description of the star-point sets via threshold segmentation and morphological algorithms based on geometricinvariant 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 registrationparameter 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 sequencestarry images with the low SNR and complex backgrounds can extract feature points more precisely and more robustlycomparing with the traditional methods. So, it is suitable for the large FOV spatial observation or surveillance applications.