Noise in digital image processing is a noise that occurs at pixel values due to random colour intensity. Several types of noise models include Gaussian noise, speckle noise, impulse noise, and Poisson noise. Before processing image data, a noise reduction process is required. One of the noise reduction algorithms used for gaussian noise models is Non-local Mean. This algorithm performs calculations sequentially on each pixel in the search block. Due to a large number of pixels and search block area in the image, the noise reduction process using the Non-local Mean algorithm is very slow. This study proposes the concept of parallel calculations for the Non-local Mean algorithm. This concept divides the search block into three parts and performs calculations on each part simultaneously. The experimental results show that the Non-local Mean algorithm with parallel calculations can reduce noise up to 30% faster if the noise standard deviation is above 30.