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.
A new SAR image classification method is proposed based on the remarkable correlation of feature channels, which are
obtained by dividing the Fourier plane into sectors according to the frequency and direction. Compared with other
feature correlation based methods in wavelet and Contourlet domain, our method operates directly in the Fourier plane,
which is more robust with regard to images size and more flexible in the capture of frequency and directional
information. Moreover, the use of FFT transform is computationally more attractive. Experimental results on the Brodatz
textures and SAR images demonstrate the effectiveness and efficiency of the proposed method.
The nonlocal (NL) means filter as a recent denoising approach has demonstrated its empirical merit for additive Gaussian
noise. In this paper, a new nonlocal means despeckling method for synthetic aperture radar (SAR) image is proposed,
which is adapted to the multiplicative model of speckle noise. The proposed method still uses Euclidean distance based
similarity measure but adopting a strategy of pixel classification, which can effectively reduce the influence of the
multiplicative speckle model and improve the effectiveness in searching of similar patches, thus contributes to the final
results. By this strategy, image pixels are first classified into different classes such as point, line, edge, etc., and then
different smooth parameters of nonlocal means filter are used according to the class information. In addition, a searching
method for rotation-invariant similar patches is designed through the use of directional information. We validate the
proposed method on real synthetic aperture radar (SAR) images and confirm the excellent despeckling performance
through comparisons with other classical despeckling methods, such the Enhanced Lee filter, Enhanced Gamma MAP
filter, wavelet thresholding, as well as original NL mean filter.
Most watermarking systems available have only a secret key, which can not be public. But in some applications watermark needs to be retrieved by public keys. How to generating public keys without weakening the performance of the private key is a key problem. In this paper a secret key watermarking system is designed, in which a novel method of generating public keys is proposed. The identifier (ID) embedded can be reliably retrieved using public keys without resorting to the original data. Because only part of embedding information is used in public keys, the above problem is successfully solved. Experimental results show its security and validity.