Abstract：Aiming at the noise image with rich texture and edge feature, an adaptive thresholding image denoising method based on morphological component analysis (MCA) and contourlet transform is proposed. Firstly, MCA method is introduced to separate the image into the low frequency part and the high frequency part. Then, an adaptive thresholding processing method is designed. Finally, according to the characteristics of noise distribution, the threshold estimation and contourlet transform are used in the low frequency part and the high frequency part to effectively remove the noise from the noisy image. The experimental results on noise images illustrate that the proposed method reserves better textures and edges of the image, and its denoising performance is better than that of the mean filter, themedian filter, the wavelet multilevel threshold denoising and the contourlet multilevel threshold denoising.
纪建，许双星，李晓. 基于形态成分分析和Contourlet变换的自适应阈值图像去噪方法*[J]. 模式识别与人工智能, 2014, 27(6): 561-568.
JI Jian, XU Shuang-Xing, LI Xiao. An Adaptive Thresholding Image Denoising Method Based on Morphological Component Analysis and Contourlet Transform. , 2014, 27(6): 561-568.
2014, Vol. 27 
