In order to address the deficiencies of existing algorithms for impulse noise removal, and to further improve denoising performance and robustness, a wavelet threshold denoising algorithm for impulse noise removal is proposed in this paper. First, based on the gray-scale characteristic of impulse noise, the randomness and approximate uniformity of its distribution, the noisy pixels are identified by using statistical method. Then, a wavelet denoising method based on an adaptive threshold of the signal-to-noise intensity and a differentiable shrinkage function is used to restore the noisy pixels. The experimental results show that, compared with the existing algorithms, the image visual perception effect, peak signal-to-noise ratio and edge preservation index obtained by the proposed algorithm are greatly improved, and it has better robustness.