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Image filtering method using trimmed statistics and edge preserving

Image filtering method using trimmed statistics and edge preserving

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Image filtering is to retain the details of the image as much as possible and meanwhile suppress the noise pollution to great extent. This study presents an image filtering using the truncated statistics and edge preserving. In the first step of our method, the alpha-trimmed filter is utilized to remove a variety of types of noises; in the second step, taking the image after alpha-trimmed filtering as a guide image, the local linear model between the guide image and the target image is established; in the third step, the obtained local linear model is further simplified to reduce the time complexity; and finally, using the relationship between image local variance and the global variance, the local linear model is modified to enhance the details of the image and meanwhile remove halo phenomenon. This method has three advantages: (i) it is flexible to deal with the images stained by various types of high-intensity noise; (ii) it is effective to keep the image details and profile information, and remove the halo phenomenon; and (iii) it runs in time linear in the image size, thus its computation complexity is low. Experimental results show that the proposed filter is robust and efficient.

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