A Denoising Algorithm of Star Map Based on Wavelet Transform and Double-Window Combined Filtering

In view of the diversity of star map noise and the low signal-to-noise ratio of the star map, a denoising algorithm of star map based on the wavelet transform and double-window combined filtering is proposed. In this paper, the algorithm is combined with wavelet transform to deal with low frequency coefficients and high frequency coefficients respectively. For the similarity of the grey distribution of star map noise and star points, a double-window noise detection method is used to determine the type of pixel point, and a flexible filtering method is chosen to remove low-frequency noise. At the same time, a threshold denoising method is used to reduce high-frequency noise. The experimental results show that the filtering method in this paper is superior to other algorithms in peak signal-to-noise ratio and structural similarity analysis.