Phase denoising is a key technology for digital speckle pattern interferometry, but the existing denoising methods represented by sinecosine mean filtering and window Fourier transform filtering cannot fully meet the requirements in terms of phase fidelity, adaptive noise reduction, and ease of operation. In this article, a new adaptive denoising method is proposed. The method estimates the noise variance of a raw phase map at the first, and then performs the sinecosine transformation of the phase map to obtain two phase maps. The two phase maps are then smoothed respectively by using several layers of wavelet threshold denoising and non-local mean filtering. The two phase maps are subjected to arctangent operation and their noise variances are estimated again. The above-mentioned denoising operations are iteratively performed according to the criterion of image noise variance to realize adaptive noise reduction of the phase maps. Experimental results show that compared with the traditional sin-cosine mean filtering, The noise variance of the proposed method is reduced by 0.38, the sum of L operators is reduced by 0.2, and the SSIM is increased by 0.16. Meanwhile, the difference of image information entropy is only 0.1. This method can effectively suppress the coherent noise in the phase maps, preserve the phase edge information, and avoid phase distortion or noise residue caused by inappropriate filtering cycles.