@article {84, title = {SAR Image Denoising via Clustering-Based Principal Component Analysis}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {52}, year = {2014}, pages = {6858 - 6869}, chapter = {6858}, abstract = {

The combination of nonlocal grouping and transformed domain filtering has led to the state-of-the-art denoising techniques. In this paper, we extend this line of study to the denoising of synthetic aperture radar (SAR) images based on clustering the noisy image into disjoint local regions with similar spatial structure and denoising each region by the linear minimum mean-square error (LMMSE) filtering in principal component analysis (PCA) domain. Both clustering and denoising are performed on image patches. For clustering, to reduce dimensionality and resist the influence of noise, several leading principal components identified by the minimum description length criterion are used to feed the K-means clustering algorithm. For denoising, to avoid the limitations of the homomorphic approach, we build our denoising scheme on additive signal-dependent noise model and derive a PCA-based LMMSE denoising model for multiplicative noise. Denoised patches of all clusters are finally used to reconstruct the noise-free image. The experiments demonstrate that the proposed algorithm achieved better performance than the referenced state-of-the-art methods in terms of both noise reduction and image detail preservation.

}, keywords = {Clustering, denoising, linear minimum meansquare error (LMMSE), minimum description length (MDL), principal component analysis (PCA), speckle noise, synthetic aperture radar (SAR)}, author = {L Xu and J Li and Y Shu and J Peng} } @article {315, title = {Adaptive bilateral filtering of image signals using local phase characteristics}, journal = {Signal Processing}, volume = {88}, year = {2008}, pages = {1615 - 1619}, abstract = {

This paper presents a novel perceptually based method for noise reduction of image signals characterized by low signal to noise ratios. The proposed method exploits the local phase characteristics of an image signal to perform bilateral filtering in an adaptive manner. The proposed method takes advantage of the human perception system to preserve perceptually significant signal detail while suppressing perceptually significant noise in the image signal. Experimental results show that the proposed method is effective at removing signal noise while enhancing perceptual quality both quantitatively and qualitatively.

}, keywords = {adaptive, Bilateral filtering, denoising, Local phase}, author = {A Wong} } @article {576, title = {Multiscale statistical methods for the segmentation of signals and images}, journal = {IEEE Transactions on Image Processing}, volume = {9}, year = {2000}, pages = {456 - 468}, abstract = {

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired

}, keywords = {biomedical MRI, boundary locations, brain, denoising, error statistics, Gaussian processes, image reconstruction, image segmentation, images, inhomogeneous Gaussian Markov random fields, interference suppression, Markov processes, medical image processing, medical imaging, multiscale methods, noisy image, noisy signal, reconstruction, recursive procedures, scientific applications, Segmentation, signal reconstruction, signals, statistical interpretation, variational approach, variational techniques}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.826782}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} }