Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
Hyperspectral imagery restoration using nonlocal spectral spatial structured sparse representation with noise estimation
1. HYPERSPECTRAL IMAGERY RESTORATION USING NONLOCAL SPECTRAL-
SPATIAL STRUCTURED SPARSE REPRESENTATION WITH NOISE ESTIMATION
ABSTRACT:
Noise reduction is an active research area in image processing due to its importance in improving
the quality of image for object detection and classification. In this paper, we develop a sparse
representation based noise reduction method for hyperspectral imagery, which is dependent on
the assumption that the non-noise component in an observed signal can be sparsely decomposed
over a redundant dictionary while the noise component does not have this property. The main
contribution of the paper is in the introduction of nonlocal similarity and spectral-spatial
structure of hyperspectral imagery into sparse representation. Non-locality means the self-
similarity of image, by which a whole image can be partitioned into some groups containing
similar patches. The similar patches in each group are sparsely represented with a shared subset
of atoms in a dictionary making true signal and noise more easily separated. Sparse
representation with spectral-spatial structure can exploit spectral and spatial joint correlations of
hyperspectral imagery by using 3-D blocks instead of 2-D patches for sparse coding, which also
makes true signal and noise more distinguished. Moreover, hyperspectral imagery has both
signal-independent and signal-dependent noises, so a mixed Poisson and Gaussian noise model is
used. In order to make sparse representation be insensitive to the various noise distribution in
different blocks, a variance-stabilizing transformation (VST) is used to make their variance
comparable. The advantages of the proposed methods are validated on both synthetic and real
hyperspectral remote sensing data sets.
ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
BANGALORE / HYDRABAD
CELL: +91 98949 17187, +91 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 / 8111
VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com
