关键词: 图像去噪, 非局部均值滤波, 积分图, Turky函数, 结构相似性

Abstract: Non-Local Means （NLM） algorithm has good characteristic for removing noise and preserving image details. But the algorithm is time consuming and the accuracy decreases significantly with the increase of noise. Fast Non-Local Means （FNLM） algorithm speeds up operation and reduces time cost, but the performance of denoising has not improved when noise increased. Aiming at the problem, this paper proposes a novel non-local means denoising method. A new exponential-Turky kernel function is put forward by combining Turky function and exponential function, which subsitutes the original exponential kernel function in both NLM algorithm and FNLM algorithm. Furthermore, both the Structure Similarity （SSIM） and Euclidean distance are introduced to measure the similarity between image neighborhood, which make the selection of weight more reasonable, and eliminate the interference of the neighborhood with dissimmilar structure in the image, as a result, the performance of denoising is approved. The experiments carried out with images in database by adding different level of Gaussian noise, the results demonstrate that the proposed method improves denoising capacity greatly, especially for image with large noise. Additionly, the efficiency of proposed method is enhanced obviously against NLM algorithm, and the time complexity is equal to FNLM algorithm and time consumption is close to FNLM algorithm too.

Key words: image denoising, non-local means, integral images, Turky function, structure similarity