Nonlocal Means SAR Image Despeckling using Principle Neighborhood Dictionaries

摘要

The Principle Neighborhood Dictionary (PND) filter projects the image patches onto a lower dimensional subspace using Principle Component analysis (PCA), based on which the similarity measure of image patch can be computed with a higher accuracy for the nonlocal means (NLM) algorithm. In this paper, a new PND filter for synthetic aperture radar (SAR) image despeckling is presented, in which a new distance that adapts to the multiplicative speckle noise is derived. Compared with the commonly used Euclidean distance in NLM, the new distance measure improves the accuracy of the similarity measure of speckled patches in SAR images. The proposed method is validated on simulated and real SAR images through comparisons with other classical despeckling methods.