针对非局部均值(NLM)算法度量邻域块相似度不够准确的缺点,提出了一种基于差异哈希算法与汉明距离的改进NLM算法。传统算法通过欧氏距离度量邻域块之间的相似度,保持边缘和细节的能力较弱,易导致滤波后的图像模糊失真。因此引入了包含梯度信息的差异哈希算法对欧氏距离进行改进,由邻域块生成差异哈希图像,并用其汉明距离衡量邻域块的相似度。实验结果表明,对于低噪图像,本方法在去噪的同时能较好地保持细节边缘,且相较其他算法,运行速度有很大提升,具有一定的应用价值。

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.