The signal-to-noise ratio (SNR) of a star map is an important factor affecting the accuracy of star point identification. For the threshold denoising methods, the noise residual caused by the threshold selection problem in the ground all-day star map affects the accuracy of the star point centroid extraction. This study proposes a near-earth all-time star map wavelet denoising method based on the local outliers factor. The proposed method applies the local outliers factor algorithm to the wavelet denoising of the star map to perform the denoising of the ground all-time star map without threshold. Herein, the real star map is considered as the original data, and the peak SNR (PSNR) and local peak value relative error (LPVRE) are used to compare and analyze the denoising effect of the star map processed using different denoising methods. Results show that compared with the traditional mean filter and wavelet threshold denoising, this method improves the PSNR and reduces the local peak relative error, and it can more efficiently remove the background noise and retain the target information.