In order to solve the problem that the non-local mean (NLM) algorithm is not accurate enough in measuring the similarity of neighborhood blocks, an improved NLM algorithm based on difference hash algorithm and Hamming distance is proposed. Traditional algorithm measures the similarity between neighborhood blocks by Euclidean distance, and the ability to maintain edges and details is weak, resulting in blurred and distorted images after filtering. Therefore, the difference hash algorithm containing the gradient information is introduced to improve the Euclidean distance, and the Hamming distance of the difference hash value is calculated to measure the similarity of the neighborhood block. Experimental results show that this method can better maintain the edges of details well while denoising, and compared with other algorithms, the running speed of the algorithm is also greatly improved, which has certain application value.