A non-local means algorithm for image denoising using structure adaptive window

作者：Hao Hongxia; Liu Fang^*; Jiao Licheng; Wu Jie

来源：Journal of Xi'an Jiaotong University, 2013, 47(12): 71-76.

DOI：10.7652/xjtuxb201312013

• Adaptive windows
• Edge preservations
• Non local means (NLM)
• Non-local means
• Signal to noise
• Similarity measurements
• Smooth effects
• Structural similarity indices

A structure adaptive window based non-local means (SAW-NLM) algorithm for image denoising is proposed to solve the problem that the results of the similarity measurement are not accurate in the non-local means (NLM) method. The proposed algorithm divides a noisy image into two parts, structural area and non-structural area, by using the primal sketch map that is extracted from the noisy image. Then, similar samples are respectively searched in these two parts using structure direction based adaptive window and isotropic Finally, the denoising result is estimated from these samples. Moreover, the window-wise method is employed in the estimation of the interest to suppress the pseudo texture phenomenon. Since the structure direction and the gray information are jointly used in the adaptive window, the similarity can be more accurately measured. Experimental results show that the SAW-NLM algorithm has advantages in the edge preservation and smooth effects. Comparisons with the traditional NLM algorithm show that SAW-NLM method improves the peak signal-to-noise by 1.1 dB and the structural similarity index by 4.6.

• 出版日期2013
• 单位西安电子科技大学