A digital image is often deteriorated by impulse noise that may occur during processes such as transmission. An impulse noise converts the pixel data in the image into black (0) or white (255) values at a random frequency and is also called salt-and-pepper noise. In this paper, we identify the details of pixels that have been damaged by impulse noise by analyzing the frequency of the noisy image using non-harmonic analysis (NHA). From experimental results, we can confirm that this method shows superior performance compared to the recent PSNR denoising method. In addition, we show that the proposed method is particularly superior in eliminating impulse noise in images with high noise rates.