Hyperspectral imagery restoration using nonlocal spectral spatial structured sparse representation with noise estimation

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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.
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The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.

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