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Diffraction Pole density data are affected by many factors such as texture inhomogeneity, insufficient statistics, instrumental errors etc. As a result we have Pole figures (Pfs) containing data noised by an irregular component. In some cases this component may be rather strong and exert heavy influence on the modeling process making the model unstable. Thus Pfs data need to be smoothed by some methods. In this work we try to interpret Pole figure data of Titanium as images and to treat the Pfs with some widespread methods of image processing. Namely we use singular value decomposition (SVD) smoothing and a simplified variant of wavelet data processing. These methods both base on the orthogonal factorizations of the initial data. SVD smoothing Usually Pf values are presented by rectangular matrices (m×n).These matrices can be factorized as P = USV T , where U and V are orthogonal matrices (m×m) and (n×n) correspondingly. S is (m×n) diagonal matrix containing singular values. The singular values can be ranked so that

Wavelet and SVD Smoothing for Pole Figures of Titanium