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摘要
This paper presents a novel approach of image noise removal via integrating geometric morphological patch grouping and adaptive principal component analysis (PCA) transform domain choosing. Image noise removal based on PCA has acquired much attention and success because of the essential difference: the energy of signal concentrates on the small subset of PCA transformed dataset, while the energy of noise evenly spreads over the whole data set. In this paper, the noisy image will be firstly decomposed into overlap patches that contain different content and structure information. However, some of them potentially have similar geometric morphology. So, their gradient map is utilized to compute the dominant orientation of gradient field to group these geometric morphology patches. Such a grouping procedure guarantees that only similar patches are used to perform hard thresholding on the coefficients to remove the noise. Furthermore, as the result and effect of feature extraction are different in different transform domain, a proper one could be adaptively chosen for different types. Finally, a comprehensive empirical evaluation of the proposed method is carried out in terms of accuracy and visuality, and the results reveal that our method appears to be competitive with the state-of-the-art noise removal methods.
